Myosin coverage - 206 genomes with mapq3 filter
Alyssa Yoxsimer
4/9/24
# All installed under v3.6, and v4.2.2 with exception of mapproj, gdsfmt, SNPRelate
library(tidyverse); theme_set(theme_classic())## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
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## ✔ lubridate 1.9.4 ✔ tidyr 1.3.1
## ✔ purrr 1.0.4
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## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errorslibrary(fs)
library(ggpubr)
library(ggbeeswarm)
library(gdsfmt)
library(SNPRelate)## SNPRelate -- supported by Streaming SIMD Extensions 2 (SSE2)library(ggrepel)
library(cowplot); theme_set(theme_cowplot(font_size=6))##
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## stamp# File paths for gasAcu5 masked aligned reads
ga5_roi_file <- "/labs/kingsley/ambenj/myosin_dups/analysis/ecotypic_depth/gasAcuv5_C4masked_nochrY/06_samtools_coverage_roi/roi_coverage_mapq3.txt"
metadata_file <- "/labs/kingsley/ambenj/myosin_dups/analysis/ecotypic_depth/metadata_227genomes.txt"
ga5_sim_roi_file <- "/labs/kingsley/ambenj/myosin_dups/analysis/ecotypic_depth/depth_simulations/206_genomes/02_coverage/stickleback_v5_assembly_MYH3C4dup_hardmasked_noChrY/roi_coverage_mapq3.txt"# File paths for SNP data
#vcf_file_1 <- "/labs/kingsley/ambenj/myosin/ecotypic_depth_gasAcu1-4/227_genomes.final.filtered_chrXIX_2614822-2744076.vcf"
vcf_file_1 <- "/labs/kingsley/ambenj/myosin_dups/analysis/assemblies/gasAcu1-4/227_genomes.final.filtered.MYHSensitiveEcopeak.noDup.recode.vcf"
het_file <- "/labs/kingsley/ambenj/myosin_dups/analysis/assemblies/gasAcu1-4/227_genomes.final.filtered.MYHSensitiveEcopeak.noDup.het"# Read roi file for gasAcu5 masked aligned reads
ga5_roi <- read_tsv(ga5_roi_file) %>%
mutate(bam = str_remove_all(bam, ".recal.realignGA5_C4masked.sort.merged.mkdup.bam|.recal.realignGA5_C4masked.sort.mkdup.bam|.recal.realignGA5_C4masked.sort.mkdup_mapq3_roi_coverage.txt|05_mkdup_index/"),
type = "real") %>%
rename(samp = bam)## Rows: 1816 Columns: 12
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: "\t"
## chr (4): desc, region, chr, bam
## dbl (8): startpos, endpos, numreads, covbases, coverage, meandepth, meanbase...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.ga5_roi## # A tibble: 1,816 × 13
## desc region chr startpos endpos numreads covbases coverage meandepth
## <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 NLRC5 chrXI… chrX… 2601498 2.63e6 2206 28701 90.9 5.06
## 2 MYH3C1 chrXI… chrX… 2637324 2.65e6 835 11352 76.0 4.11
## 3 MYH3C2 chrXI… chrX… 2655758 2.67e6 558 9018 83.0 3.74
## 4 MYH3C3 chrXI… chrX… 2667295 2.68e6 567 8397 78.3 3.88
## 5 duplicatio… chrXI… chrX… 2665791 2.68e6 939 14097 80.0 3.91
## 6 SYT19 chrXI… chrX… 2697955 2.73e6 1383 21276 65.1 3.09
## 7 CALB2A chrXI… chrX… 2745064 2.76e6 1193 14557 99.7 6.09
## 8 HTRA1A chrVI… chrVI 14382418 1.44e7 1297 16460 96.1 5.65
## 9 NLRC5 chrXI… chrX… 2601498 2.63e6 2593 30765 97.5 6.18
## 10 MYH3C1 chrXI… chrX… 2637324 2.65e6 1042 13930 93.3 5.24
## # ℹ 1,806 more rows
## # ℹ 4 more variables: meanbaseq <dbl>, meanmapq <dbl>, samp <chr>, type <chr># Read simulations roi file for gasAcu5 masked aligned reads
ga5_sim_roi <- read_tsv(ga5_sim_roi_file) %>%
mutate(bam = str_remove_all(bam, ".RG.sorted.bam|/labs/kingsley/ambenj/myosin_dups/analysis/ecotypic_depth/depth_simulations/206_genomes/01_alignment/stickleback_v5_assembly_MYH3C4dup_hardmasked_noChrY/"),
type = "sim") %>%
rename(samp = bam)## Rows: 88 Columns: 12
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: "\t"
## chr (4): desc, region, chr, bam
## dbl (8): startpos, endpos, numreads, covbases, coverage, meandepth, meanbase...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.ga5_sim_roi## # A tibble: 88 × 13
## desc region chr startpos endpos numreads covbases coverage meandepth
## <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 NLRC5 chrXI… chrX… 2601498 2.63e6 3042 28712 91.0 6.98
## 2 MYH3C1 chrXI… chrX… 2637324 2.65e6 1265 11214 75.1 6.21
## 3 MYH3C2 chrXI… chrX… 2655758 2.67e6 728 9274 85.3 4.96
## 4 MYH3C3 chrXI… chrX… 2667295 2.68e6 824 8341 77.8 5.70
## 5 duplicatio… chrXI… chrX… 2665791 2.68e6 1374 13856 78.6 5.77
## 6 SYT19 chrXI… chrX… 2697955 2.73e6 1740 17756 54.3 3.84
## 7 CALB2A chrXI… chrX… 2745064 2.76e6 1515 14467 99.1 7.78
## 8 HTRA1A chrVI… chrVI 14382418 1.44e7 1761 16829 98.2 7.74
## 9 NLRC5 chrXI… chrX… 2601498 2.63e6 3038 28434 90.1 6.94
## 10 MYH3C1 chrXI… chrX… 2637324 2.65e6 1074 11324 75.8 5.23
## # ℹ 78 more rows
## # ℹ 4 more variables: meanbaseq <dbl>, meanmapq <dbl>, samp <chr>, type <chr># Combine roi tables from real and sim samples
ga5_comb_roi <- rbind(ga5_roi, ga5_sim_roi)
ga5_comb_roi %>%
filter(type=="sim")## # A tibble: 88 × 13
## desc region chr startpos endpos numreads covbases coverage meandepth
## <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 NLRC5 chrXI… chrX… 2601498 2.63e6 3042 28712 91.0 6.98
## 2 MYH3C1 chrXI… chrX… 2637324 2.65e6 1265 11214 75.1 6.21
## 3 MYH3C2 chrXI… chrX… 2655758 2.67e6 728 9274 85.3 4.96
## 4 MYH3C3 chrXI… chrX… 2667295 2.68e6 824 8341 77.8 5.70
## 5 duplicatio… chrXI… chrX… 2665791 2.68e6 1374 13856 78.6 5.77
## 6 SYT19 chrXI… chrX… 2697955 2.73e6 1740 17756 54.3 3.84
## 7 CALB2A chrXI… chrX… 2745064 2.76e6 1515 14467 99.1 7.78
## 8 HTRA1A chrVI… chrVI 14382418 1.44e7 1761 16829 98.2 7.74
## 9 NLRC5 chrXI… chrX… 2601498 2.63e6 3038 28434 90.1 6.94
## 10 MYH3C1 chrXI… chrX… 2637324 2.65e6 1074 11324 75.8 5.23
## # ℹ 78 more rows
## # ℹ 4 more variables: meanbaseq <dbl>, meanmapq <dbl>, samp <chr>, type <chr># Read whole genome files for gasAcu5 masked aligned reads
ga5_wg_files <- dir_ls(path = "/labs/kingsley/ambenj/myosin_dups/analysis/ecotypic_depth/gasAcuv5_C4masked_nochrY/06_samtools_coverage_wg/", glob = "*mapq3_wg_coverage.txt")
ga5_sim_wg_files <- dir_ls(path = "/labs/kingsley/ambenj/myosin_dups/analysis/ecotypic_depth/depth_simulations/206_genomes/02_coverage/stickleback_v5_assembly_MYH3C4dup_hardmasked_noChrY/", glob = "*mapq3_wg_coverage.txt")
# function to add file name to dataframe
read_and_record_filename <- function(filename){
read_tsv(filename) %>%
mutate(filename = path_file(filename))
}
# gather real wg files into dataframe
ga5_wg <- map_df(ga5_wg_files, read_and_record_filename)%>%
mutate(samp = str_remove_all(filename, ".recal.realignGA5_C4masked.sort.merged.mkdup_mapq3_wg_coverage.txt|.recal.realignGA5_C4masked.sort.mkdup_mapq3_wg_coverage.txt|../ecotypic_depth/gasAcuv5_C4masked_nochrY/06_samtools_coverage_wg"))
ga5_wg## # A tibble: 5,221 × 11
## chr startpos endpos numreads covbases coverage meandepth meanbaseq meanmapq
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 chrI 1 2.96e7 2276751 28423668 96.0 5.74 27.2 57.8
## 2 chrII 1 2.37e7 1791949 22810471 96.3 5.65 27.2 58.7
## 3 chrI… 1 1.78e7 1368279 16754126 94.3 5.76 27.2 57.1
## 4 chrIV 1 3.42e7 2570661 32232528 94.3 5.60 27.2 57.8
## 5 chrIX 1 2.08e7 1640991 19485000 93.5 5.88 27.2 56.8
## 6 chrM 1 1.57e4 18096 14042 89.2 86.7 27.3 49.7
## 7 chrUn 1 1.99e7 1111456 9958942 50.1 4.12 27.2 36.7
## 8 chrV 1 1.56e7 1208332 14698641 94.5 5.80 27.1 57
## 9 chrVI 1 1.88e7 1420598 17972608 95.5 5.64 27.2 58.5
## 10 chrV… 1 3.08e7 2322042 29052786 94.4 5.62 27.2 57.9
## # ℹ 5,211 more rows
## # ℹ 2 more variables: filename <chr>, samp <chr># Gather simualated wg files into dataframe
ga5_sim_wg <- map_df(ga5_sim_wg_files, read_and_record_filename)%>%
mutate(samp = str_remove_all(filename, ".mapq3_wg_coverage.txt"))
ga5_sim_wg## # A tibble: 253 × 11
## chr startpos endpos numreads covbases coverage meandepth meanbaseq meanmapq
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 chrI 1 2.96e7 3010567 28374766 95.8 7.63 35.3 58.5
## 2 chrII 1 2.37e7 2424802 22882221 96.6 7.69 35.3 58.9
## 3 chrI… 1 1.78e7 1795109 16838045 94.8 7.59 35.3 58.3
## 4 chrIV 1 3.42e7 3454294 32344585 94.6 7.57 35.3 58.2
## 5 chrIX 1 2.08e7 2069660 19488540 93.5 7.45 35.3 58.2
## 6 chrM 1 1.57e4 756 10047 63.8 3.64 35.3 49.2
## 7 chrUn 1 1.99e7 711199 7345105 36.9 2.66 35.3 46.5
## 8 chrV 1 1.56e7 1579597 14729260 94.7 7.62 35.3 58
## 9 chrVI 1 1.88e7 1905219 18005937 95.6 7.61 35.3 58.7
## 10 chrV… 1 3.08e7 3095846 29174676 94.8 7.54 35.3 58.3
## # ℹ 243 more rows
## # ℹ 2 more variables: filename <chr>, samp <chr># combine real and simulated data into one dataframe
ga5_comb_wg <- rbind(ga5_wg, ga5_sim_wg)
ga5_comb_wg## # A tibble: 5,474 × 11
## chr startpos endpos numreads covbases coverage meandepth meanbaseq meanmapq
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 chrI 1 2.96e7 2276751 28423668 96.0 5.74 27.2 57.8
## 2 chrII 1 2.37e7 1791949 22810471 96.3 5.65 27.2 58.7
## 3 chrI… 1 1.78e7 1368279 16754126 94.3 5.76 27.2 57.1
## 4 chrIV 1 3.42e7 2570661 32232528 94.3 5.60 27.2 57.8
## 5 chrIX 1 2.08e7 1640991 19485000 93.5 5.88 27.2 56.8
## 6 chrM 1 1.57e4 18096 14042 89.2 86.7 27.3 49.7
## 7 chrUn 1 1.99e7 1111456 9958942 50.1 4.12 27.2 36.7
## 8 chrV 1 1.56e7 1208332 14698641 94.5 5.80 27.1 57
## 9 chrVI 1 1.88e7 1420598 17972608 95.5 5.64 27.2 58.5
## 10 chrV… 1 3.08e7 2322042 29052786 94.4 5.62 27.2 57.9
## # ℹ 5,464 more rows
## # ℹ 2 more variables: filename <chr>, samp <chr>Add metadata
metadata <- read_tsv(metadata_file)## Rows: 227 Columns: 16
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: "\t"
## chr (9): seq_ID, pop, coord_approx, mar_fresh, notes, water_type, used_joint...
## dbl (7): GPS_north, GPS_east, PNW_independent_MvsF_c150, NorthEurope_indepen...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.metadata## # A tibble: 227 × 16
## seq_ID pop coord_approx GPS_north GPS_east mar_fresh notes water_type
## <chr> <chr> <chr> <dbl> <dbl> <chr> <chr> <chr>
## 1 AKMA_X_2001… Alas… <NA> 60.1 -149. M <NA> Marine
## 2 AKST_X_2001… Alas… <NA> 60.1 -149. F <NA> River
## 3 ANSR_X_2009… Anse… <NA> 54.1 -132. F <NA> Freshwater
## 4 BARW_X_2012… Barr… <NA> 71.3 -157. M <NA> Marine
## 5 BHAR_X_2011… Nort… <NA> 57.6 -7.28 F <NA> Lake
## 6 BIGR_1_32_2… Big … <NA> 39.3 -124. M <NA> Marine
## 7 BIGR_52_54_… Big … <NA> 39.3 -124. F <NA> River
## 8 BIGR_1_32_2… Big … <NA> 39.3 -124. M <NA> Marine
## 9 BIGR_1_32_2… Big … <NA> 39.3 -124. M <NA> Marine
## 10 BIGR_3_63_2… Big … <NA> 39.3 -124. M <NA> Marine
## # ℹ 217 more rows
## # ℹ 8 more variables: PNW_independent_MvsF_c150 <dbl>,
## # NorthEurope_independent_MvsF_c151 <dbl>,
## # CaliforniaFreshwater_vs_AllPacificMarine_c153 <dbl>,
## # c154_globalsuperglacial_FvsM <dbl>, c155_global_FvsM <dbl>,
## # used_joint_genotyping <chr>, `used _river_comparisons` <chr>,
## # used_pilot_analysis <chr>Is the depth consistent across chromosomes?
# Plot coverage by chromosome
ga5_wg %>%
filter(!chr %in% c("chrM", "chrUn")) %>%
ggplot(aes(chr, meandepth)) +
geom_point() +
coord_flip() +
facet_wrap(~samp)
Samples that look like they are actually males:
- BIGR_1_32_2007_02
- BK70_X_2010_02
- BNST_X_2006_10
- LAUR_X_1993_9_5
- MIDF_REND_2011_05
- MIDF_REND_2011_06
- NTRW_X_X_02
- REIV_X_2011_04
# Plot coverage by chromosome for two normal samples and the samples that look like males
ga5_wg %>%
filter(!chr %in% c("chrM", "chrUn"),
samp %in% c("AKMA_X_2001_102", "AKST_X_2001_03", "BIGR_1_32_2007_02", "BK70_X_2010_02", "BNST_X_2006_10", "LAUR_X_1993_9_5", "MIDF_REND_2011_05", "MIDF_REND_2011_06", "NTRW_X_X_02", "REIV_X_2011_04")) %>%
ggplot(aes(chr, meandepth)) +
geom_point() +
coord_flip() +
facet_wrap(~samp, nrow=1)
Some samples look like they have very uneven coverage…why would this be?
# get whole genome mean depth for gasAcu5 masked aligned (excluding chrUn, chrM, chrY) per sample
ga5_wg_cov <- ga5_comb_wg %>%
filter(!chr %in% c("chrUn", "chrM", "chrY")) %>%
group_by(samp) %>%
summarize(weighted_mean_depth = weighted.mean(meandepth, endpos))
ga5_wg_cov## # A tibble: 238 × 2
## samp weighted_mean_depth
## <chr> <dbl>
## 1 AKMA_X_2001_102 5.66
## 2 AKST_X_2001_03 6.12
## 3 ANSR_X_2009_01 10.3
## 4 ANTL 0.733
## 5 BARW_X_2012_04 5.71
## 6 BDGB 1.46
## 7 BEPA 1.21
## 8 BHAR_X_2011_02 5.21
## 9 BIGL 2.34
## 10 BIGR 1.61
## # ℹ 228 more rows# Add additional depth and metadata info to rois
ga5_roi_cov <- left_join(ga5_comb_roi, ga5_wg_cov) %>%
left_join(., metadata, by = c("samp" = "seq_ID")) %>%
mutate(wg_norm_depth = meandepth / weighted_mean_depth,
ecotype = case_when(mar_fresh == "M" ~ "Marine",
mar_fresh == "F" ~ "Freshwater",
type == "sim" ~ "Simulation"),
ecotype = as.factor(ecotype),
ecotype = relevel(ecotype, "Marine"),
sex = case_when(samp %in% c("BIGR_1_32_2007_02", "BK70_X_2010_02", "BNST_X_2006_10", "LAUR_X_1993_9_5", "MIDF_REND_2011_05", "MIDF_REND_2011_06", "NTRW_X_X_02", "REIV_X_2011_04", "rabs_THREE_spine.male.fa_sim4X.rep0", "stickleback_v5-BOULxBDGB_F5-4copy.male.fa_sim4X.rep0", "stickleback_v5-BEPA-AF_ONT_M1020-5copy.male.fa_sim4X.rep0", "stickleback_v5-BLAU22_12-6copy.male.fa_sim4X.rep0") ~ "male",
TRUE ~ "female"),
samp_length = str_length(samp)) %>%
separate(samp, into = "acronym", remove=FALSE, sep="_")## Joining with `by = join_by(samp)`## Warning: Expected 1 pieces. Additional pieces discarded in 1752 rows [1, 2, 3, 4, 5, 6,
## 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, ...].ga5_roi_cov %>%
filter(sex=="female") %>%
# mutate(desc = fct_relevel(desc, "eda", "nlrc5", "calb2a")) %>%
ggplot(aes(reorder(samp, wg_norm_depth), wg_norm_depth, color = ecotype)) +
geom_point() +
scale_color_manual(values=c("#d73027","#0072b2", "black")) +
theme(axis.text.x=element_text(angle = -90, hjust = 0)) +
facet_wrap(~desc, ncol = 1) +
xlab("Individual") +
ylab("Normalized depth (1x genome coverage)")
# Plot just the male samples for gasAcu5 masked aligned
ga5_roi_cov %>%
filter(sex=="male") %>%
# mutate(desc = fct_relevel(desc, "eda", "nlrc5", "calb2a")) %>%
ggplot(aes(reorder(samp, wg_norm_depth), wg_norm_depth, color = ecotype)) +
geom_point() +
scale_color_manual(values=c("#d73027","#0072b2", "black")) +
theme(axis.text.x=element_text(angle = -90, hjust = 0)) +
facet_wrap(~desc, ncol = 11) +
xlab("Individual") +
ylab("Normalized depth (1x genome coverage)")
# Plot just the simulated samples for gasAcu5 masked aligned
ga5_roi_cov %>%
filter(type=="sim") %>%
# mutate(desc = fct_relevel(desc, "eda", "nlrc5", "calb2a")) %>%
ggplot(aes(reorder(samp, wg_norm_depth), wg_norm_depth)) +
geom_point() +
theme(axis.text.x=element_text(angle = -90, hjust = 0)) +
facet_wrap(~desc, ncol = 11) +
xlab("Individual") +
ylab("Normalized depth (1x genome coverage)")
ga5_roi_cov %>%
filter(desc == "HTRA1A") %>%
ggplot(aes(fct_reorder(samp, wg_norm_depth), wg_norm_depth, color = ecotype)) +
geom_point() +
scale_color_manual(values=c("#d73027","#0072b2", "black")) +
theme(axis.text.x=element_text(angle = -90, hjust = 0), legend.position = c(0.025, 0.75)) +
ylim(0,3) +
ggtitle("HTRA1 read depth") +
xlab("Individual") +
ylab("Normalized depth (1x genome coverage)")## Warning: A numeric `legend.position` argument in `theme()` was deprecated in ggplot2
## 3.5.0.
## ℹ Please use the `legend.position.inside` argument of `theme()` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
# Get average from simulations
sim_average <- ga5_roi_cov %>%
filter(type=="sim", desc=="MYH3C3") %>%
mutate(CN = case_when(str_detect(samp, "3copy") ~ "3-copy",
str_detect(samp, "4copy") ~ "4-copy",
str_detect(samp, "5copy") ~ "5-copy",
str_detect(samp, "6copy") ~ "6-copy")) %>%
group_by(CN, sex) %>%
summarise(wg_norm_depth = mean(wg_norm_depth))## `summarise()` has grouped output by 'CN'. You can override using the `.groups`
## argument.sim_average## # A tibble: 4 × 3
## # Groups: CN [4]
## CN sex wg_norm_depth
## <chr> <chr> <dbl>
## 1 3-copy female 0.763
## 2 4-copy female 1.48
## 3 5-copy female 2.33
## 4 6-copy female 3.07# Plot MYH3C3 read depth for all samples
ga5_roi_cov %>%
filter(desc == "MYH3C3", sex=="female", type=="real") %>%
ggplot(aes(fct_reorder(samp, wg_norm_depth), wg_norm_depth, color = ecotype)) +
geom_hline(data = sim_average, aes(yintercept = wg_norm_depth), linetype = "dashed", color = "grey") +
geom_point() +
scale_color_manual(values=c("#d73027","#0072b2", "black")) +
theme(axis.text.x=element_text(angle = -90, hjust = 0), legend.position = c(0.025, 0.75)) +
xlab("Individual") +
ylab("Normalized MYH3C3 read depth")
# Plot MYH3C3 read depth for all samples except those used in river comparisons
ga5_roi_cov %>%
filter(desc == "MYH3C3",
sex=="female",
type=="real",
is.na(`used _river_comparisons`)) %>%
ggplot(aes(fct_reorder(samp, wg_norm_depth), wg_norm_depth, color = ecotype)) +
geom_hline(data = sim_average, aes(yintercept = wg_norm_depth), linetype = "dashed", color = "grey") +
geom_point() +
scale_color_manual(values=c("#d73027","#0072b2", "black")) +
theme(axis.text.x=element_text(angle = -90, hjust = 0), legend.position = c(0.03, 0.75)) +
xlab("Individual") +
ylab("Normalized MYH3C3 read depth")
# Plot MYH3C3 read depth for all samples except those used in river comparisons and the low depth original Jones et al. 2012 samples
ga5_roi_cov %>%
filter(desc == "MYH3C3",
sex=="female",
type=="real",
is.na(`used _river_comparisons`),
samp_length > 6) %>%
ggplot(aes(fct_reorder(samp, wg_norm_depth), wg_norm_depth, color = ecotype)) +
geom_hline(data = sim_average, aes(yintercept = wg_norm_depth), linetype = "dashed", color = "grey") +
geom_point() +
scale_color_manual(values=c("#d73027","#0072b2", "black")) +
theme(axis.text.x=element_text(angle = -90, hjust = 0), legend.position = c(0.04, 0.79)) +
xlab("Individual") +
ylab("Normalized MYH3C3 read depth")
# Plot MYH3C3 read depth for MAYR lake
ga5_roi_cov %>%
filter(desc == "MYH3C3",
sex=="female",
type=="real",
acronym=="MAYR",
samp_length > 6) %>%
ggplot(aes(fct_reorder(samp, wg_norm_depth), wg_norm_depth, color = ecotype)) +
geom_hline(data = sim_average, aes(yintercept = wg_norm_depth), linetype = "dashed", color = "grey") +
geom_point() +
scale_color_manual(values=c("#0072b2")) +
theme(axis.text.x=element_text(angle = -90, hjust = 0), legend.position = c(0.04, 0.79)) +
xlab("Individual") +
ylab("Normalized MYH3C3 read depth")
# Plot MYH3C3 read depth for all samples except those used in river comparisons and the low depth original Jones et al. 2012 samples
C3_global <- ga5_roi_cov %>%
filter(desc == "MYH3C3",
sex=="female",
type=="real",
c155_global_FvsM %in% c(0,1),
samp_length > 6) %>%
ggplot(aes(fct_reorder(samp, wg_norm_depth), wg_norm_depth, color = ecotype)) +
geom_hline(data = sim_average, aes(yintercept = wg_norm_depth), linetype = "dashed", color = "grey") +
geom_point() +
scale_color_manual(values=c("#d73027","#0072b2", "black")) +
theme_cowplot(6) +
theme(axis.text.x=element_text(angle = -90, hjust = 0), legend.position = c(0.07, 0.79)) +
xlab("Individual") +
ylab("Normalized MYH3C3 read depth")
C3_global
ga5_roi_cov %>%
filter(desc == "MYH3C3", type=="real", sex=="female", c155_global_FvsM %in% c(0,1), samp_length > 6) %>%
ggscatter(x = "GPS_north", y = "wg_norm_depth",
color = "ecotype", palette = c("#d73027","#0072b2"),
add = "reg.line", conf.int = TRUE,
xlab = "Latitude (°N)",
ylab = "Normalized depth of MYH3C3\n(1x genome coverage)") +
stat_cor(aes(color = ecotype), label.x = 30) +
geom_hline(data = sim_average, aes(yintercept = wg_norm_depth), linetype = "dashed", color = "grey")
Pacific_PNW_lat <- ga5_roi_cov %>%
filter(desc == "MYH3C3", type=="real", sex=="female", PNW_independent_MvsF_c150 %in% c(0,1), samp_length > 6) %>%
ggscatter(x = "GPS_north", y = "wg_norm_depth",
color = "ecotype", palette = c("#d73027","#0072b2"),
add = "reg.line", conf.int = TRUE,
xlab = "Latitude",
ylab = "Normalized depth of MYH3C3") +
stat_cor(aes(color = ecotype)) +
geom_hline(data = sim_average, aes(yintercept = wg_norm_depth), linetype = "dashed", color = "grey")
Pacific_PNW_lat
# Plot without original 21 genomes and including California
Pacific_CA_lat <- ga5_roi_cov %>%
filter(desc == "MYH3C3", type=="real", sex=="female", PNW_independent_MvsF_c150 %in% c(0,1) | CaliforniaFreshwater_vs_AllPacificMarine_c153 %in% c(0,1), samp_length > 6) %>%
ggscatter(x = "GPS_north", y = "wg_norm_depth",
color = "ecotype", palette = c("#d73027","#0072b2"),
add = "reg.line", conf.int = TRUE,
xlab = "Latitude",
ylab = "Normalized depth of MYH3C3") +
stat_cor(aes(color = ecotype))+
geom_hline(data = sim_average, aes(yintercept = wg_norm_depth), linetype = "dashed", color = "grey")
Pacific_CA_lat
# Plot without original 21 genomes and for populations glaciated during the last ice age
ga5_roi_cov %>%
filter(desc == "MYH3C3", type=="real", sex=="female", c154_globalsuperglacial_FvsM %in% c(0,1), samp_length > 6) %>%
ggscatter(x = "GPS_north", y = "wg_norm_depth",
color = "ecotype", palette = c("#d73027","#0072b2"),
add = "reg.line", conf.int = TRUE,
xlab = "Latitude",
ylab = "Normalized depth of MYH3C3\n(1x genome coverage)") +
stat_cor(aes(color = ecotype)) +
geom_hline(data = sim_average, aes(yintercept = wg_norm_depth), linetype = "dashed", color = "grey")
# Plot without original 21 samples
Europe_lat <- ga5_roi_cov %>%
filter(desc == "MYH3C3", type=="real", sex=="female", NorthEurope_independent_MvsF_c151 %in% c(0,1), samp_length > 6) %>%
ggscatter(x = "GPS_north", y = "wg_norm_depth",
color = "ecotype", palette = c("#d73027","#0072b2"),
add = "reg.line", conf.int = TRUE,
xlab = "Latitude",
ylab = "MYH3C3 normalized depth") +
stat_cor(aes(color = ecotype)) +
geom_hline(data = sim_average, aes(yintercept = wg_norm_depth), linetype = "dashed", color = "grey")
Europe_lat
# Plot without original 21 samples
test <- ga5_roi_cov %>%
filter(desc == "MYH3C3", type=="real", sex=="female", c155_global_FvsM %in% c(0,1), samp_length >6) %>%
ggplot(aes(ecotype, wg_norm_depth, color = ecotype)) +
geom_hline(data = sim_average, aes(yintercept = wg_norm_depth), linetype = "dashed", color = "grey") +
geom_beeswarm(size = 3, cex=2.5,alpha = 0.7) +
geom_boxplot(fill = NA) +
scale_color_manual(values=c("#d73027","#0072b2")) +
xlab("Ecotype") +
ylab("MYH3C3 normalized depth") +
theme_cowplot(6) +
theme(legend.position = "top") +
stat_compare_means(aes(group = ecotype), label.x = 1, label.y = 2.6, size = 3)
test
ga5_roi_cov %>%
filter(desc == "MYH3C3", type=="real", sex=="female", str_detect(samp, "LOBG")) %>%
ggplot(aes(pop, wg_norm_depth, color = pop)) +
geom_hline(data = sim_average, aes(yintercept = wg_norm_depth), linetype = "dashed", color = "grey") +
geom_beeswarm(size = 3, cex=2.5,alpha = 0.7) +
geom_boxplot(fill = NA) +
scale_color_manual(values=c("#d73027","#0072b2")) +
ggtitle("MYH3C3 read depth") +
xlab("Ecotype") +
ylab("Normalized depth of MYH3C3 \n(1x genome coverage)") +
theme(legend.position = "none",
text = element_text(size=16)) +
stat_compare_means(aes(group = ecotype), label.x = 1, label.y = 2.6, size = 5)## Warning: Unknown or uninitialised column: `p`.## Warning: Computation failed in `stat_compare_means()`.
## Caused by error:
## ! argument "x" is missing, with no default
# Make wider version of table to plot correlation between different genes
ga5_roi_cov_wide <- ga5_roi_cov %>%
select(desc, samp:type, weighted_mean_depth:sex) %>%
pivot_wider(names_from = desc, values_from = wg_norm_depth)# Plot pearson correlation between MYH3C3 read depth and duplication region
ga5_roi_cov_wide %>%
filter(sex=="female", type=="real") %>%
ggscatter(x = "MYH3C3", y = "duplication_region",
add = "reg.line", conf.int = TRUE, alpha=0.5) +
stat_cor()
Final figure plots
# Plot MYH3C3 read depth for balanced global selection, excluding low depth original Jones et al. 2012 samples
ga5_roi_cov_filt <- ga5_roi_cov %>%
filter(desc == "MYH3C3", type=="real", sex=="female", NorthEurope_independent_MvsF_c151 %in% c(0,1)|PNW_independent_MvsF_c150 %in% c(0,1)|CaliforniaFreshwater_vs_AllPacificMarine_c153 %in% c(0,1), samp_length > 6) %>%
mutate(Individual = case_when(samp == "BIGR_1_32_2007_03" ~ "BIGR_M",
samp == "BIGR_52_54_2008_02" ~ "BIGR_F",
samp == "LITC_0_05_2008_841" ~ "LITC_M",
samp == "LITC_23_32_2008_306" ~ "LITC_F",
samp == "MIDF_BLUP_2011_01" ~ "MIDF_M",
samp == "MIDF_REND_2011_01" ~ "MIDF_F",
samp == "TYNE_1_2001_14" ~ "TYNE_M",
samp == "TYNE_8_2003_902" ~ "TYNE_F",
TRUE ~ acronym))
# Wilcoxon test for marine vs freshwater
w <- wilcox.test(subset(ga5_roi_cov_filt, ecotype == "Freshwater")$wg_norm_depth, subset(ga5_roi_cov_filt, ecotype == "Marine")$wg_norm_depth, alternative = "greater")
w##
## Wilcoxon rank sum test with continuity correction
##
## data: subset(ga5_roi_cov_filt, ecotype == "Freshwater")$wg_norm_depth and subset(ga5_roi_cov_filt, ecotype == "Marine")$wg_norm_depth
## W = 1211, p-value = 4.917e-08
## alternative hypothesis: true location shift is greater than 0C3_global <- ga5_roi_cov_filt %>%
ggplot(aes(fct_reorder(Individual, wg_norm_depth), wg_norm_depth, color = ecotype)) +
geom_hline(data = sim_average, aes(yintercept = wg_norm_depth), linetype = "dashed", color = "grey", linewidth=0.25) +
geom_segment( aes(x=Individual, xend=Individual, y=0, yend=wg_norm_depth)) +
geom_point(size=1.5) +
scale_color_manual(values=c("#d73027","#0072b2"), name = "Ecotype") +
# scale_shape_manual(values = c(21,19)) +
scale_y_continuous(limits = c(0,3.7), expand = expansion(mult = c(0, 0))) +
theme_cowplot(6) +
panel_border(color="black", size=0.75) +
theme(axis.text.x=element_text(angle = -90, hjust = 0),
legend.position = c(0.02, 0.8),
legend.box.background = element_rect(color="black", linewidth=0.25, fill = "white"),
legend.box.margin = margin(4, 4, 4, 4)) +
xlab("Individual") +
ylab("MYH3C3 normalized depth")
# ylim(0,3.5)
C3_global
# Plot with facet
lat <- ga5_roi_cov %>%
filter(desc == "MYH3C3", type=="real", sex=="female", NorthEurope_independent_MvsF_c151 %in% c(0,1)|PNW_independent_MvsF_c150 %in% c(0,1)|CaliforniaFreshwater_vs_AllPacificMarine_c153 %in% c(0,1), samp_length > 6) %>%
mutate(ocean = case_when(PNW_independent_MvsF_c150 %in% c(0,1)|CaliforniaFreshwater_vs_AllPacificMarine_c153 %in% c(0,1) ~ "Pacific",
NorthEurope_independent_MvsF_c151 %in% c(0,1) ~ "Atlantic")) %>%
ggplot(aes(GPS_north, wg_norm_depth, color = ecotype)) +
geom_hline(data = sim_average, aes(yintercept = wg_norm_depth), linetype = "dashed", color = "grey", linewidth=0.25) +
geom_point(alpha=0.7, size=1.5) +
geom_smooth(aes(fill = ecotype), method = "lm") +
stat_cor(aes(color = ecotype), size=2) +
scale_color_manual(values=c("#d73027","#0072b2"),) +
scale_fill_manual(values=c("#d73027","#0072b2")) +
scale_y_continuous(limits = c(0,3.7), expand = expansion(mult = c(0, 0))) +
facet_wrap(~factor(ocean, levels=c("Pacific", "Atlantic")), scales = "free_x") +
theme_cowplot(6) +
panel_border(color="black", size=0.75) +
theme(legend.position = "none") +
xlab("Latitude (°N)") +
ylab("MYH3C3 normalized depth")
lat## `geom_smooth()` using formula = 'y ~ x'
hz_plot <- ga5_roi_cov %>%
filter(desc == "MYH3C3", sex == "female", !is.na(`used _river_comparisons`), samp_length > 6) %>%
mutate(location = as.factor(case_when(acronym == "BIGR" ~ "Big River,\nCA, USA",
acronym %in% c("BNMA", "BNST") ~ "Bonsall River,\nBC, Canada",
acronym == "LITC" ~ "Little Campbell River,\nBC, Canada",
acronym == "TYNE" ~ "Tyne River,\nScotland, UK",
acronym == "MIDF" ~ "Midfjardara River,\nIceland")),
location = fct_relevel(location, "Big River,\nCA, USA", "Little Campbell River,\nBC, Canada", "Bonsall River,\nBC, Canada", "Tyne River,\nScotland, UK", "Midfjardara River,\nIceland")) %>%
ggplot(aes(location, wg_norm_depth, color = ecotype)) +
geom_hline(data = sim_average, aes(yintercept = wg_norm_depth), linetype = "dashed", color = "grey", linewidth=0.25) +
geom_beeswarm(cex=2, dodge.width = .7, alpha = 0.7) +
geom_boxplot(fill = NA, outlier.shape = NA) +
scale_color_manual(values=c("#d73027","#0072b2")) +
scale_y_continuous(limits = c(0,3.7), expand = expansion(mult = c(0, 0))) +
# ylim(0,2.5) +
ylab("MYH3C3 normalized depth") +
theme_cowplot(6) +
panel_border(color="black", size=0.75) +
theme(legend.position = "none",
axis.title.x=element_blank()) +
stat_compare_means(aes(group = ecotype), label.x = 1, label.y = 3.3, size = 3, label="p.signif")
hz_plot
# Arrange plots using cowplot
# align all plots vertically
plots <- align_plots(test, C3_global, lat, hz_plot, align = 'v', axis = 'l')## `geom_smooth()` using formula = 'y ~ x'top_row <- plot_grid(
plots[[2]],
labels = c("B"),
label_size = 12,
nrow = 1
)
bottom_row <- plot_grid(
plots[[3]], plots[[4]],
labels = c("C", "D"),
rel_widths = c(1, 1),
label_size = 12,
nrow = 1
)
final_plot <- plot_grid(top_row, bottom_row, ncol = 1, rel_heights = c(1.1, 1), label_size = 12)
final_plot
# Save ASE figure to separate file
ggsave(
"/labs/kingsley/ambenj/myosin_dups/analysis/ecotypic_depth/gasAcuv5_C4masked_nochrY/ecotypic_depth_figure.pdf",
plot = final_plot,
scale = 1,
width = 6.5,
height = 3.75,
units = c("in"),
dpi = 300,
)PCA of myosin region
# Reformat (biallelic version, can also try "copy.num.of.ref")
snpgdsVCF2GDS(vcf_file_1, "all.gds", method="biallelic.only")## Start file conversion from VCF to SNP GDS ...
## Method: extracting biallelic SNPs
## Number of samples: 227
## Parsing "/labs/kingsley/ambenj/myosin_dups/analysis/assemblies/gasAcu1-4/227_genomes.final.filtered.MYHSensitiveEcopeak.noDup.recode.vcf" ...
## import 3570 variants.
## + genotype { Bit2 227x3570, 197.8K } *
## Optimize the access efficiency ...
## Clean up the fragments of GDS file:
## open the file 'all.gds' (221.0K)
## # of fragments: 47
## save to 'all.gds.tmp'
## rename 'all.gds.tmp' (220.7K, reduced: 324B)
## # of fragments: 20# Summary
snpgdsSummary("all.gds")## The file name: /oak/stanford/scg/lab_kingsley/ambenj/myosin_dups/scripts/R/all.gds
## The total number of samples: 227
## The total number of SNPs: 3570
## SNP genotypes are stored in SNP-major mode (Sample X SNP).# Open GDS file
genofile <- snpgdsOpen("all.gds")
genofile## File: /oak/stanford/scg/lab_kingsley/ambenj/myosin_dups/scripts/R/all.gds (220.7K)
## + [ ] *
## |--+ sample.id { Str8 227 LZMA_ra(35.7%), 1.2K }
## |--+ snp.id { Int32 3570 LZMA_ra(12.8%), 1.8K }
## |--+ snp.rs.id { Str8 3570 LZMA_ra(2.97%), 113B }
## |--+ snp.position { Int32 3570 LZMA_ra(27.8%), 3.9K }
## |--+ snp.chromosome { Str8 3570 LZMA_ra(0.94%), 141B }
## |--+ snp.allele { Str8 3570 LZMA_ra(15.4%), 2.1K }
## |--+ genotype { Bit2 227x3570, 197.8K } *
## \--+ snp.annot [ ]
## |--+ qual { Float32 3570 LZMA_ra(82.8%), 11.6K }
## \--+ filter { Str8 3570 LZMA_ra(0.80%), 149B }# Run PCA
pca <- snpgdsPCA(genofile, num.thread=2, autosome.only = FALSE)## Principal Component Analysis (PCA) on genotypes:
## Excluding 11 SNPs (monomorphic: TRUE, MAF: NaN, missing rate: NaN)
## # of samples: 227
## # of SNPs: 3,559
## using 2 threads
## # of principal components: 32
## PCA: the sum of all selected genotypes (0,1,2) = 1237565
## CPU capabilities: Double-Precision SSE2
## Sun Jul 13 22:16:21 2025 (internal increment: 4292)
## [..................................................] 0%, ETC: --- [==================================================] 100%, completed, 0s
## Sun Jul 13 22:16:21 2025 Begin (eigenvalues and eigenvectors)
## Sun Jul 13 22:16:21 2025 Done.#pca_filter <- snpgdsPCA(genofile, num.thread=2, autosome.only = FALSE, sample.id = vcf_samples_filt)
snpgdsClose(genofile)# variance proportion (%)
pc.percent <- round(pca$varprop*100, 2)
PC1_lab <- paste("PC1 (", pc.percent[1], "%)", sep = "")
PC2_lab <- paste("PC2 (", pc.percent[2], "%)", sep = "")# make a data.frame
tab <- data.frame(sample.id = pca$sample.id,
EV1 = pca$eigenvect[,1], # the first eigenvector
EV2 = pca$eigenvect[,2], # the second eigenvector
EV3 = pca$eigenvect[,3], # the third eigenvector
EV4 = pca$eigenvect[,4], # the fourth eigenvector
stringsAsFactors = FALSE)
tab_annot <- tab %>%
mutate(samp = str_replace_all(sample.id, '\\|', '_'),
samp = str_replace_all(samp, '\\#', '_')) %>%
left_join(., filter(ga5_roi_cov, desc == "MYH3C3"), by=c("samp" = "samp")) %>%
left_join(., read_tsv(het_file), by=c("sample.id" = "INDV")) %>%
mutate(prop_hom = `O(HOM)`/N_SITES,
prop_het = 1-prop_hom,
het_status = ifelse(F > 0.5, "likely-hom", "likely-het"))## Rows: 227 Columns: 5
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: "\t"
## chr (1): INDV
## dbl (4): O(HOM), E(HOM), N_SITES, F
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.tab_annot## sample.id EV1 EV2 EV3 EV4
## 1 AKMA|X|2001#102 0.1229973506 -0.036164554 0.0078482840 -5.829536e-03
## 2 AKST|X|2001#03 -0.0540685701 -0.024170294 0.0154518328 -2.967874e-03
## 3 ANSR_X_2009#01 -0.0488148378 -0.020515347 0.0134344962 -2.303888e-03
## 4 ANTL 0.0011383959 -0.007256721 0.0027692207 1.486978e-04
## 5 BARW|X|2012#04 0.1203047443 -0.036465805 0.0068169850 1.544402e-03
## 6 BDGB 0.0107605097 -0.010982033 0.0075335915 -2.826808e-03
## 7 BEPA -0.0326665285 -0.013685672 0.0079011114 -6.228288e-05
## 8 BHAR|X|2011#02 0.1255649180 -0.043802700 0.0055753083 3.035664e-03
## 9 BIGL -0.0146005684 0.044801849 0.0143877877 -3.729064e-03
## 10 BIGR -0.0129065216 0.023308549 0.0090199669 9.761090e-03
## 11 BIGR_1_32_2007#03 0.0100751593 0.115785628 0.0183156456 -1.042762e-01
## 12 BIGR_52_54_2008#02 0.0091435651 0.144795196 0.0263159483 2.408555e-01
## 13 BIGR|1_32|2007#01 0.0188560556 0.163583424 0.0278809268 -7.274894e-02
## 14 BIGR|1_32|2007#02 0.0096217805 0.132945593 -0.4035082338 -1.201959e-01
## 15 BIGR|3_63|2007#08 0.0539102017 0.072841120 0.0179592043 -1.438120e-02
## 16 BIGR|3_63|2007#14 0.0162503009 0.164434928 0.0344149784 1.335552e-02
## 17 BIGR|52_54|2007#04 0.0199480738 0.201728347 0.0330942137 3.714994e-01
## 18 BIGR|52_54|2007#05 0.0168073085 0.199775348 0.0325064511 3.498671e-01
## 19 BIGR|52_54|2007#12 0.0204752622 0.188043740 0.0337738891 1.870450e-01
## 20 BIGR|52_54|2007#17 0.0193218937 0.203851980 0.0323342876 3.709131e-01
## 21 BITJ|X|X#17 0.1346372052 -0.048492985 0.0040052506 3.326175e-03
## 22 BK70_X_2010#02 -0.0283131466 -0.005219098 -0.1517376652 9.148944e-04
## 23 BKW2_X_2010#01 -0.0515444654 -0.023705532 0.0149340190 -2.477336e-04
## 24 BLAU|X|2002#08 -0.0533887089 -0.025331736 0.0003418651 5.497741e-03
## 25 BNMA|X|2006#01 0.1208561114 -0.035868220 0.0072909423 -6.392751e-03
## 26 BNMA|X|2006#02 0.0184182283 -0.026312279 0.0121381272 -1.900110e-03
## 27 BNMA|X|2006#03 0.0535496055 0.070169853 0.0113359189 8.263133e-02
## 28 BNMA|X|2006#05 0.1126502304 -0.034097088 0.0070829646 4.879090e-03
## 29 BNMA|X|2006#07 0.1135331914 -0.032266081 0.0064769735 -2.671297e-03
## 30 BNST|X2006#08 -0.0529718339 -0.024397827 0.0137248419 -3.288410e-04
## 31 BNST|X|2006#01 -0.0529862572 -0.024345390 0.0135640139 -9.531572e-04
## 32 BNST|X|2006#06 -0.0525914568 -0.024007193 0.0131488791 -4.456826e-04
## 33 BNST|X|2006#09 -0.0512432789 -0.022789173 0.0138752525 1.516363e-03
## 34 BNST|X|2006#10 0.0099585403 0.121795420 -0.3909912501 1.226684e-01
## 35 BOOT|X|2011#05 -0.0537923751 -0.023945030 0.0124212473 -3.239073e-04
## 36 BOUL_X_2010#01 -0.0554163990 -0.024579621 0.0130122832 3.423150e-04
## 37 BOUL_X_2010#02 -0.0546592346 -0.024001761 0.0122813819 5.449902e-04
## 38 BRLY|X|2012#01 -0.0529800504 -0.023330502 0.0138376564 -2.540152e-04
## 39 BRNT_X_2009#05 -0.0537791757 -0.023642201 0.0151197604 -6.788869e-04
## 40 BSEA|55|2011#01 0.1146099896 -0.032726173 0.0012339467 -2.822104e-03
## 41 CERC|X|X#04 0.0150473718 0.181450131 0.0266545316 -1.619563e-01
## 42 CHRU|X|2011#04 -0.0472742454 -0.023408963 0.0074123675 2.322476e-03
## 43 CMCB|X|2011#02 -0.0511776015 -0.021976694 0.0123998837 -8.962840e-04
## 44 COAT|X|2009#90234 -0.0544979801 -0.024109283 0.0134946980 2.058328e-04
## 45 COND|X|2002#14 -0.0165439282 0.054370003 0.0059781057 1.197715e-02
## 46 CORC|X|2011#01 -0.0538772484 -0.024066537 0.0147682569 -2.535499e-03
## 47 DAIM|X|2011#03 -0.0485920578 -0.023510551 0.0075031240 1.374020e-03
## 48 DANS|X|X#05 -0.0536869287 -0.022934091 0.0146383326 -4.918477e-04
## 49 DARW_X_2009#03 0.1221097573 -0.037327091 0.0056668373 1.784648e-03
## 50 DAWS_X_2009#02 0.1212210407 -0.039273920 0.0079360364 -3.933452e-03
## 51 DNSE|X|2011#07 -0.0539374615 -0.024327501 0.0108074822 -1.343635e-03
## 52 DRIZ_I_2010#01 -0.0539405025 -0.022378795 0.0146928717 -3.882860e-03
## 53 DRIZ_L_2009#02 -0.0543924964 -0.024114791 0.0147243476 -3.909349e-04
## 54 DRIZ_L_2009#15 -0.0543716626 -0.023402939 0.0143440581 -9.711323e-04
## 55 DRIZ_O_2009#03 -0.0546591182 -0.024216776 0.0145536656 -1.757859e-04
## 56 ECHO|X|2011#03 -0.0526502771 -0.023792102 0.0130957275 -6.015032e-04
## 57 EDEN_X_2010#01 -0.0545903673 -0.023886257 0.0133033399 9.069261e-04
## 58 ERBC|X|X#8770 0.0218436125 0.199461326 0.0233117106 -2.013031e-01
## 59 ESCP|X|1993#75_8 -0.0529991857 -0.024039468 0.0128710847 7.328591e-04
## 60 FADA_FJ_2001#17 0.1234581311 -0.043492981 0.0033309470 3.536780e-03
## 61 FRIC_X_2003#C10 0.0210042351 0.186926137 0.0333053677 -2.363568e-01
## 62 FRIL|X|X#05 0.0220998474 0.197572307 0.0314944070 -2.462027e-01
## 63 FTC -0.0450298157 -0.018586042 0.0108523618 1.868220e-04
## 64 GARC|X|X#711 0.0171456832 0.193255122 0.0268114408 -1.832810e-01
## 65 GIFU|X|2000#03 0.0344312621 -0.018539311 -0.0148748179 -9.878367e-03
## 66 GJOG 0.0126323868 -0.015858257 0.0068858055 8.668851e-04
## 67 GOLD_X_2009#02 -0.0550466419 -0.023966956 0.0148388445 -5.511251e-04
## 68 GOLD|X|X#12 -0.0538821254 -0.023721245 0.0140645557 -6.686325e-04
## 69 GORT 0.0117742608 -0.014586619 0.0071487048 1.640883e-03
## 70 HRUN|X|X#02 -0.0522988712 -0.025186502 0.0020171448 4.783066e-03
## 71 HSTA|X|2011#03 0.1201473600 -0.046694824 0.0041802924 2.758695e-03
## 72 HUTU -0.0411524933 -0.010459260 -0.0046190031 -3.068075e-03
## 73 JADE|X|2011#05 -0.0542258002 -0.023775805 0.0149158665 -1.057878e-03
## 74 JAMA 0.0179212208 -0.016033945 0.0078648278 -1.362649e-03
## 75 KALD|X|2010#03 0.1195940425 -0.045219893 0.0033144384 2.837464e-03
## 76 KFSY|X|2011#05 -0.0542054499 -0.023424570 0.0154360662 -1.815958e-03
## 77 KODK|X|2004#04 0.1205462090 -0.038354471 0.0090635596 -6.057208e-03
## 78 LAMH|X|2001#09 0.1198858623 -0.044161032 0.0049028973 2.977919e-03
## 79 LANO|X|2001#05 0.1301576262 -0.045422110 0.0039122304 5.373039e-03
## 80 LAUR|X|1993#9_5 -0.0397689507 0.001839763 -0.3877767113 4.616418e-03
## 81 LITC_0_05_2008#841 0.0824038832 -0.022602518 0.0074676941 -7.694479e-04
## 82 LITC_0_05_2008#842 0.1006939327 -0.029728891 0.0083962867 -8.215602e-03
## 83 LITC_0_05_2008#FL 0.0598516612 -0.019483763 0.0080299973 -7.095817e-03
## 84 LITC_0_05_2008#FO 0.0933551100 -0.027439810 0.0077745078 -1.881018e-03
## 85 LITC_0_45_2008#789 0.1100719330 -0.031031553 0.0087006289 -5.269072e-03
## 86 LITC_23_32_2008#306 -0.0514082072 -0.023220768 0.0128376143 6.586722e-04
## 87 LITC_23_32_2008#324 -0.0514211599 -0.022351299 0.0139097974 1.058069e-03
## 88 LITC_23_32_2008#347 -0.0492161275 -0.021898513 0.0124763258 -7.291652e-04
## 89 LITC_23_32_2008#356 -0.0538756467 -0.023869897 0.0136214212 4.280138e-04
## 90 LITC_23_32_2008#744 -0.0480746753 -0.020886640 0.0136387262 -3.164768e-04
## 91 LLYD|X|2003#03 0.1341702214 -0.046180063 0.0029823540 -4.424733e-04
## 92 LMCK|X|X#03 -0.0536085538 -0.023355038 0.0155885846 -2.926902e-03
## 93 LMIA|X|X#02 -0.0491031542 -0.022586274 0.0059791697 2.468869e-03
## 94 LMOR|X|2001#12 0.1411295477 -0.049419539 0.0050817848 2.713848e-03
## 95 LNEA|M|2001#03 -0.0506689379 -0.023968727 0.0049919401 3.286377e-04
## 96 LNGH|X|2011#01 -0.0477761230 -0.022691217 0.0067536914 1.801911e-03
## 97 LOBG|X|1999#09 0.0188487824 -0.030340851 0.0109251693 -2.224118e-03
## 98 LOBG|X|1999#11 0.0188761398 -0.029952313 0.0114392873 -8.916531e-04
## 99 LOBG|X|1999#12 -0.0549376683 -0.024633472 0.0154262737 4.649244e-04
## 100 LOBG|X|1999#13 0.0216457047 -0.031999913 0.0124611879 -5.515466e-03
## 101 LOBG|X|1999#32 0.1274285075 -0.039904049 0.0062832994 -5.545157e-03
## 102 LOBG|X|1999#36 0.0197616971 -0.031386905 0.0099676992 -4.854873e-04
## 103 LOBG|X|1999#38 -0.0545691675 -0.024466393 0.0154091014 -2.215222e-03
## 104 LOBG|X|1999#39 -0.0545797410 -0.024727973 0.0150833629 -1.285857e-03
## 105 LOBG|X|1999#43 0.0168090077 -0.029772543 0.0116109170 -2.119536e-03
## 106 LOBG|X|1999#46 0.1221599096 -0.039180276 0.0078398469 -2.643573e-03
## 107 LOBG|X|2012#02 0.0151026709 -0.030879997 0.0093240564 -3.595356e-03
## 108 LOBG|X|2012#03 0.0162657384 -0.031676713 0.0106977770 -2.394643e-04
## 109 LOBG|X|2012#04 0.0175649445 -0.032833888 0.0102746698 -6.026403e-04
## 110 LOBG|X|2012#05 0.0197965176 -0.033475537 0.0100408700 -1.041734e-03
## 111 LOBG|X|2012#06 -0.0531329345 -0.023854646 0.0150194745 1.233439e-04
## 112 LOBG|X|2012#08 0.0158972062 -0.030004031 0.0080919255 -1.575447e-04
## 113 LOBG|X|2012#11 0.0183350649 -0.029153647 0.0099840714 -2.340545e-03
## 114 LOBG|X|2012#17 0.1293235720 -0.036736327 0.0051465189 1.262938e-03
## 115 LOBG|X|2012#26 -0.0547043270 -0.024746457 0.0153238845 1.240199e-03
## 116 LOBG|X|2012#28 -0.0530485766 -0.023889090 0.0155534302 -2.691765e-03
## 117 LONG|X|2011#01 -0.0540299851 -0.023854379 0.0150967490 -2.413141e-04
## 118 LSHP|X|2011#01 -0.0545186964 -0.024686819 0.0153740582 -3.343674e-04
## 119 LSOL|X|2012#04 0.0179523696 0.188140675 0.0298965616 -9.516079e-02
## 120 LSWL|X|2011#05 0.1219687261 -0.043192268 0.0064893072 2.815703e-03
## 121 LTLT|X|2011#06 0.1252918284 -0.048155517 0.0042314494 2.433812e-03
## 122 LUTE|X|2003#76_4 -0.0530458790 -0.023599776 0.0142530186 -6.539849e-04
## 123 MANC|X|X#05 0.1273952564 -0.035381704 0.0070818148 -9.624805e-04
## 124 MATA -0.0146244603 0.041602369 0.0096448740 3.338899e-02
## 125 MAYR_X_2009#05 -0.0543291135 -0.024619912 0.0145727108 2.771166e-05
## 126 MAYR_X_2009#11 -0.0533908849 -0.023817174 0.0141142778 -1.015736e-03
## 127 MAYR|X|2004#03 -0.0546484546 -0.024315798 0.0148621449 -4.636820e-04
## 128 MAYR|X|2004#04 -0.0545867628 -0.024553022 0.0150553046 -4.503567e-04
## 129 MAYR|X|2004#09 -0.0546152015 -0.024555665 0.0144160149 -5.120682e-04
## 130 MAYR|X|2004#13 -0.0542225823 -0.024196477 0.0144950603 -4.270297e-04
## 131 MAYR|X|2004#14 -0.0556696099 -0.024821896 0.0143215356 -1.158318e-05
## 132 MAYR|X|2004#17 -0.0547633043 -0.024023120 0.0139654990 -2.235552e-04
## 133 MAYR|X|2004#21 -0.0534549533 -0.024030085 0.0147299548 -1.547950e-03
## 134 MAYR|X|2004#24 -0.0541329258 -0.023449081 0.0156478145 -2.122685e-03
## 135 MAYR|X|2004#26 -0.0553860723 -0.024682910 0.0136718948 -1.018044e-03
## 136 MAYR|X|2004#30 -0.0546858628 -0.024049248 0.0152484419 -5.382417e-04
## 137 MDPC_X_1993#01 0.1221369408 -0.035079671 0.0093607112 -5.148071e-03
## 138 MIDF|BDVW|2011#01 0.1288056360 -0.045787672 0.0044438622 3.179096e-03
## 139 MIDF|BDVW|2011#02 0.1215247248 -0.043578414 0.0057871196 2.670490e-03
## 140 MIDF|BLUP|2011#01 0.1276349014 -0.044915830 0.0023601099 4.398966e-03
## 141 MIDF|REND|2011#01 -0.0527109886 -0.024553967 0.0002417791 5.161337e-03
## 142 MIDF|REND|2011#04 -0.0523874580 -0.024791347 0.0002892275 4.492321e-03
## 143 MIDF|REND|2011#05 -0.0378162539 0.001327743 -0.3630701841 2.909402e-03
## 144 MIDF|REND|2011#06 -0.0369577664 0.002129532 -0.3977887358 1.176006e-03
## 145 MIDF|REND|2011#10 -0.0530082446 -0.024739161 -0.0011919452 3.103180e-03
## 146 MIDF|S101|2011#05 0.1340617609 -0.048516645 0.0027759198 4.824048e-03
## 147 MIDF|S101|2011#06 0.1219804840 -0.042971945 0.0051715352 1.847599e-03
## 148 MNKA|X|X#02 -0.0537144601 -0.024011742 0.0153993810 -1.341745e-03
## 149 MNYN|X|X#29997 -0.0539172320 -0.024549142 0.0154611457 -1.076145e-03
## 150 MUDL -0.0362087366 -0.014598379 0.0105827350 -5.469889e-04
## 151 NEU -0.0004348792 -0.001741896 -0.0003003470 -7.818434e-08
## 152 NHBR|X|2003#05 0.1343332453 -0.044036640 0.0027895381 1.843385e-03
## 153 NOST -0.0129257638 -0.005508874 0.0025932446 1.097840e-03
## 154 NTRW|X|X#02 0.0615196477 -0.013067369 -0.2445474850 5.805627e-03
## 155 OBSE|X|2011#05 0.1260187910 -0.044039467 0.0044400565 2.278548e-03
## 156 OLNY_X_2007#03 0.0061191064 0.089801879 0.0200339388 -1.017618e-01
## 157 ORPH|X|2011#05 -0.0527867581 -0.023318379 0.0135834121 1.987529e-04
## 158 PAXB -0.0385152486 -0.015429982 0.0109726455 -1.180371e-03
## 159 PINC|X|X#03 0.0165511404 0.181985844 0.0280113570 -1.785564e-01
## 160 POQU|X|2009#29995 0.1158296486 -0.037573652 0.0073528385 -4.911781e-03
## 161 QUIN_X_2003#02 -0.0396555673 -0.011087627 -0.0124920922 -3.443325e-03
## 162 RABS 0.0427617896 -0.021475673 0.0076283003 -3.216202e-05
## 163 RCAL|X|2001#24 -0.0511533879 -0.023190643 0.0002052008 -6.553078e-05
## 164 RDSP_X_2010#02 -0.0543239223 -0.024002994 0.0145644988 -3.017552e-04
## 165 RDSP|X|2012#70307 -0.0553133333 -0.024538213 0.0147432766 -9.149840e-04
## 166 RDSP|X|2012#70311 -0.0519428548 -0.022710912 0.0119789488 -1.395656e-03
## 167 RDSP|X|2012#70314 -0.0531178033 -0.024296077 0.0144699062 -3.074004e-04
## 168 RDSP|X|2012#70332 -0.0538035786 -0.023895006 0.0145725536 -4.707764e-04
## 169 RDSP|X|2012#70337 -0.0536995371 -0.023852563 0.0149776593 -1.038817e-03
## 170 RDSP|X|2012#70346 -0.0537310332 -0.024138227 0.0141448408 -9.914177e-04
## 171 RDSP|X|2012#70353 -0.0527601710 -0.022827588 0.0150099982 -1.705027e-03
## 172 RDSP|X|2012#70364 -0.0487828462 -0.022372891 0.0147039512 -1.806339e-03
## 173 RDSP|X|2012#70368 -0.0541435589 -0.024288322 0.0148096487 -1.004834e-03
## 174 RDSP|X|2012#70385 -0.0548325594 -0.024624477 0.0149252752 -6.872845e-04
## 175 REIV|X|2011#04 -0.0043821176 -0.014494088 -0.3355559907 8.309768e-03
## 176 RGLN|X|2011#04 -0.0470000637 -0.021420364 0.0043038152 3.995610e-03
## 177 ROUG|X|2000#f03 -0.0543993759 -0.023976501 0.0145950898 1.397784e-03
## 178 SACK|X|2010#0898 0.0173504106 0.171998509 0.0284039902 2.255911e-01
## 179 SALR 0.0056227378 -0.009007944 0.0061523099 -2.313452e-04
## 180 SALS|X|X#01 0.0181552697 0.181022285 0.0282870812 -6.651498e-02
## 181 SAPC|X|X#01 0.0196026242 0.189444659 0.0305258197 -1.992952e-01
## 182 SCRM|X|2010#873 0.0161646657 0.174950119 0.0398962466 7.423526e-02
## 183 SCRS|X|2009#8253 0.0176966730 0.191527324 0.0443751987 1.021591e-01
## 184 SCX -0.0296059448 -0.012660128 0.0055928598 6.513099e-04
## 185 SDPY_X_2006#24 -0.0544893081 -0.024130390 0.0128986189 1.217778e-03
## 186 SHEL -0.0376379989 -0.015407154 0.0068247791 4.824211e-04
## 187 SHEL_L_2008#01 -0.0457973371 -0.021009936 0.0047979028 8.453732e-04
## 188 SJCR|X|2009#8212 0.0181230015 0.169225007 0.0371114665 4.985315e-02
## 189 SKID_X_2009#01 -0.0540699925 -0.024307124 0.0145563667 -2.585706e-04
## 190 SKON|X|X#12 -0.0540916283 -0.024004917 0.0143760734 -2.692839e-05
## 191 SLMW|X|2001#0918 0.0142021341 0.170061571 0.0381698220 7.694062e-02
## 192 SLTC|X|X#90458 -0.0545302965 -0.023979041 0.0151563429 -5.011579e-04
## 193 SLVR|X|2009#29996 -0.0524237621 -0.023244845 0.0137386238 -2.534658e-03
## 194 SPNC_L_2009#01 0.0229204239 -0.031064612 0.0110028176 -3.147588e-03
## 195 SPNC_L_2009#02 0.1236537330 -0.037970157 0.0053251730 -6.999372e-03
## 196 SPNC_O_2010#08 -0.0537737420 -0.023580248 0.0134936717 -1.836477e-04
## 197 SPNC_O_2010#14 -0.0539744755 -0.024187565 0.0152573685 -4.930224e-04
## 198 SRLY|X|2011#01 -0.0543868276 -0.023660655 0.0129955083 -5.262443e-04
## 199 SRST|S1|2000#05 -0.0512944947 -0.021221098 0.0137104541 -2.590570e-03
## 200 SSMC|X|2010#01 0.0171410153 0.178347738 0.0302385646 -1.625698e-01
## 201 STIU_X_2009#03 -0.0540795444 -0.023981465 0.0153905380 -1.046128e-03
## 202 STMY|X|2011#03 -0.0538690885 -0.023705339 0.0138629855 -1.521701e-03
## 203 SUNC|X|X#04 0.0174810880 0.181743454 0.0265979662 -1.929919e-01
## 204 TERN|X|2011#02 -0.0543036522 -0.024080951 0.0154890530 3.513938e-04
## 205 TORM|X|2011#03 0.1352875342 -0.048408205 0.0046540809 4.670028e-03
## 206 TYNE_1 -0.0155822835 -0.009737746 0.0066137083 1.721318e-04
## 207 TYNE_1_2001#07 0.1250792601 -0.042873131 0.0040834768 1.132979e-03
## 208 TYNE_1_2001#08 0.1270558318 -0.043187860 0.0058939621 2.961360e-03
## 209 TYNE_1_2001#09 0.1222189641 -0.041909478 0.0050350283 1.557924e-03
## 210 TYNE_1_2001#10 0.0368497016 -0.020448130 0.0066621347 -1.757134e-05
## 211 TYNE_1_2001#14 0.1245663930 -0.044622550 0.0045116833 2.753956e-03
## 212 TYNE_8 -0.0411281632 -0.018370599 0.0087983229 2.738803e-04
## 213 TYNE_8_2003#902 -0.0479378263 -0.023876691 0.0071050538 2.869180e-03
## 214 TYNE_8_2003#906 -0.0494166718 -0.023333392 0.0075873828 1.085205e-03
## 215 TYNE_8_2003#908 -0.0514697034 -0.023840957 0.0057694274 1.838622e-03
## 216 TYNE_8_2003#919 -0.0499742114 -0.023951387 0.0058869463 2.783142e-03
## 217 TYNE_8_2003#920 -0.0483164593 -0.023715955 0.0077519857 1.420775e-03
## 218 URRI|X|X#07 -0.0526167455 -0.023567643 0.0011568094 3.475026e-03
## 219 VIFC|X|X#07 -0.0525368015 -0.024983062 0.0038021345 3.412131e-03
## 220 VIFR|X|X#08 -0.0534552539 -0.025905769 0.0035849283 2.532127e-03
## 221 WALC|X|2005#05 -0.0524548859 -0.023001474 0.0157191153 -1.769597e-03
## 222 WALR|X|2005#32 -0.0522518248 -0.023067242 0.0153355758 -3.020248e-03
## 223 WATT_X_2009#05 -0.0486892039 -0.021163281 0.0134540604 -1.014668e-04
## 224 WDPL_X_2009#05 -0.0501780091 -0.021590388 0.0141068543 2.935659e-05
## 225 WMSO_X_2002#bigf -0.0007224793 0.074346590 0.0216650424 4.705335e-02
## 226 WOLF|X|2011#04 -0.0523354208 -0.023158633 0.0149208406 -3.211175e-03
## 227 ZERO|X|2011#05 -0.0533838390 -0.023806598 0.0158872763 -1.193097e-03
## samp desc region chr startpos endpos
## 1 AKMA_X_2001_102 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 2 AKST_X_2001_03 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 3 ANSR_X_2009_01 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 4 ANTL MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 5 BARW_X_2012_04 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 6 BDGB MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 7 BEPA MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 8 BHAR_X_2011_02 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 9 BIGL MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 10 BIGR MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 11 BIGR_1_32_2007_03 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 12 BIGR_52_54_2008_02 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 13 BIGR_1_32_2007_01 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 14 BIGR_1_32_2007_02 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 15 BIGR_3_63_2007_08 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 16 BIGR_3_63_2007_14 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 17 BIGR_52_54_2007_04 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 18 BIGR_52_54_2007_05 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 19 BIGR_52_54_2007_12 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 20 BIGR_52_54_2007_17 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 21 BITJ_X_X_17 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 22 BK70_X_2010_02 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 23 BKW2_X_2010_01 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 24 BLAU_X_2002_08 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 25 BNMA_X_2006_01 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 26 BNMA_X_2006_02 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 27 BNMA_X_2006_03 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 28 BNMA_X_2006_05 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 29 BNMA_X_2006_07 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 30 BNST_X2006_08 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 31 BNST_X_2006_01 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 32 BNST_X_2006_06 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 33 BNST_X_2006_09 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 34 BNST_X_2006_10 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 35 BOOT_X_2011_05 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 36 BOUL_X_2010_01 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 37 BOUL_X_2010_02 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 38 BRLY_X_2012_01 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 39 BRNT_X_2009_05 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 40 BSEA_55_2011_01 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 41 CERC_X_X_04 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 42 CHRU_X_2011_04 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 43 CMCB_X_2011_02 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 44 COAT_X_2009_90234 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 45 COND_X_2002_14 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 46 CORC_X_2011_01 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 47 DAIM_X_2011_03 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 48 DANS_X_X_05 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 49 DARW_X_2009_03 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 50 DAWS_X_2009_02 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 51 DNSE_X_2011_07 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 52 DRIZ_I_2010_01 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 53 DRIZ_L_2009_02 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 54 DRIZ_L_2009_15 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 55 DRIZ_O_2009_03 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 56 ECHO_X_2011_03 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 57 EDEN_X_2010_01 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 58 ERBC_X_X_8770 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 59 ESCP_X_1993_75_8 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 60 FADA_FJ_2001_17 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 61 FRIC_X_2003_C10 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 62 FRIL_X_X_05 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 63 FTC MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 64 GARC_X_X_711 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 65 GIFU_X_2000_03 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 66 GJOG MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 67 GOLD_X_2009_02 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 68 GOLD_X_X_12 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 69 GORT MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 70 HRUN_X_X_02 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 71 HSTA_X_2011_03 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 72 HUTU MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 73 JADE_X_2011_05 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 74 JAMA MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 75 KALD_X_2010_03 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 76 KFSY_X_2011_05 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 77 KODK_X_2004_04 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 78 LAMH_X_2001_09 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 79 LANO_X_2001_05 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 80 LAUR_X_1993_9_5 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 81 LITC_0_05_2008_841 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 82 LITC_0_05_2008_842 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 83 LITC_0_05_2008_FL MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 84 LITC_0_05_2008_FO MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 85 LITC_0_45_2008_789 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 86 LITC_23_32_2008_306 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 87 LITC_23_32_2008_324 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 88 LITC_23_32_2008_347 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 89 LITC_23_32_2008_356 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 90 LITC_23_32_2008_744 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 91 LLYD_X_2003_03 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 92 LMCK_X_X_03 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 93 LMIA_X_X_02 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 94 LMOR_X_2001_12 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 95 LNEA_M_2001_03 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 96 LNGH_X_2011_01 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 97 LOBG_X_1999_09 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 98 LOBG_X_1999_11 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 99 LOBG_X_1999_12 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 100 LOBG_X_1999_13 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 101 LOBG_X_1999_32 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 102 LOBG_X_1999_36 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 103 LOBG_X_1999_38 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 104 LOBG_X_1999_39 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 105 LOBG_X_1999_43 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 106 LOBG_X_1999_46 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 107 LOBG_X_2012_02 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 108 LOBG_X_2012_03 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 109 LOBG_X_2012_04 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 110 LOBG_X_2012_05 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 111 LOBG_X_2012_06 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 112 LOBG_X_2012_08 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 113 LOBG_X_2012_11 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 114 LOBG_X_2012_17 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 115 LOBG_X_2012_26 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 116 LOBG_X_2012_28 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 117 LONG_X_2011_01 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 118 LSHP_X_2011_01 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 119 LSOL_X_2012_04 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 120 LSWL_X_2011_05 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 121 LTLT_X_2011_06 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 122 LUTE_X_2003_76_4 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 123 MANC_X_X_05 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 124 MATA MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 125 MAYR_X_2009_05 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 126 MAYR_X_2009_11 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 127 MAYR_X_2004_03 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 128 MAYR_X_2004_04 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 129 MAYR_X_2004_09 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 130 MAYR_X_2004_13 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 131 MAYR_X_2004_14 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 132 MAYR_X_2004_17 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 133 MAYR_X_2004_21 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 134 MAYR_X_2004_24 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 135 MAYR_X_2004_26 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 136 MAYR_X_2004_30 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 137 MDPC_X_1993_01 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 138 MIDF_BDVW_2011_01 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 139 MIDF_BDVW_2011_02 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 140 MIDF_BLUP_2011_01 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 141 MIDF_REND_2011_01 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 142 MIDF_REND_2011_04 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 143 MIDF_REND_2011_05 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 144 MIDF_REND_2011_06 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 145 MIDF_REND_2011_10 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 146 MIDF_S101_2011_05 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 147 MIDF_S101_2011_06 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 148 MNKA_X_X_02 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 149 MNYN_X_X_29997 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 150 MUDL MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 151 NEU MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 152 NHBR_X_2003_05 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 153 NOST MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 154 NTRW_X_X_02 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 155 OBSE_X_2011_05 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 156 OLNY_X_2007_03 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 157 ORPH_X_2011_05 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 158 PAXB MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 159 PINC_X_X_03 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 160 POQU_X_2009_29995 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 161 QUIN_X_2003_02 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 162 RABS MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 163 RCAL_X_2001_24 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 164 RDSP_X_2010_02 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 165 RDSP_X_2012_70307 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 166 RDSP_X_2012_70311 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 167 RDSP_X_2012_70314 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 168 RDSP_X_2012_70332 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 169 RDSP_X_2012_70337 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 170 RDSP_X_2012_70346 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 171 RDSP_X_2012_70353 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 172 RDSP_X_2012_70364 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 173 RDSP_X_2012_70368 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 174 RDSP_X_2012_70385 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 175 REIV_X_2011_04 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 176 RGLN_X_2011_04 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 177 ROUG_X_2000_f03 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 178 SACK_X_2010_0898 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 179 SALR MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 180 SALS_X_X_01 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 181 SAPC_X_X_01 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 182 SCRM_X_2010_873 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 183 SCRS_X_2009_8253 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 184 SCX MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 185 SDPY_X_2006_24 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 186 SHEL MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 187 SHEL_L_2008_01 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 188 SJCR_X_2009_8212 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 189 SKID_X_2009_01 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 190 SKON_X_X_12 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 191 SLMW_X_2001_0918 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 192 SLTC_X_X_90458 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 193 SLVR_X_2009_29996 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 194 SPNC_L_2009_01 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 195 SPNC_L_2009_02 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 196 SPNC_O_2010_08 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 197 SPNC_O_2010_14 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 198 SRLY_X_2011_01 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 199 SRST_S1_2000_05 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 200 SSMC_X_2010_01 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 201 STIU_X_2009_03 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 202 STMY_X_2011_03 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 203 SUNC_X_X_04 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 204 TERN_X_2011_02 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 205 TORM_X_2011_03 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 206 TYNE_1 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 207 TYNE_1_2001_07 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 208 TYNE_1_2001_08 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 209 TYNE_1_2001_09 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 210 TYNE_1_2001_10 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 211 TYNE_1_2001_14 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 212 TYNE_8 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 213 TYNE_8_2003_902 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 214 TYNE_8_2003_906 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 215 TYNE_8_2003_908 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 216 TYNE_8_2003_919 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 217 TYNE_8_2003_920 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 218 URRI_X_X_07 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 219 VIFC_X_X_07 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 220 VIFR_X_X_08 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 221 WALC_X_2005_05 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 222 WALR_X_2005_32 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 223 WATT_X_2009_05 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 224 WDPL_X_2009_05 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 225 WMSO_X_2002_bigf MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 226 WOLF_X_2011_04 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 227 ZERO_X_2011_05 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## numreads covbases coverage meandepth meanbaseq meanmapq acronym type
## 1 567 8397 78.2864 3.876380 27.7 46.2 AKMA real
## 2 2059 10201 95.1054 14.412500 27.4 51.8 AKST real
## 3 2171 9737 90.7794 15.194800 23.3 51.4 ANSR real
## 4 119 2695 25.1259 0.394089 30.1 43.5 ANTL real
## 5 587 7844 73.1307 4.013980 27.0 47.6 BARW real
## 6 221 3613 33.6845 0.730934 29.9 45.2 BDGB real
## 7 594 5918 55.1743 1.974730 33.1 48.0 BEPA real
## 8 620 8482 79.0789 4.306270 27.4 47.0 BHAR real
## 9 706 5604 52.2469 2.359410 30.5 48.9 BIGL real
## 10 523 5326 49.6550 1.741560 31.2 49.2 BIGR real
## 11 553 7912 73.7647 3.648330 21.6 46.7 BIGR real
## 12 971 8886 82.8454 6.509510 21.4 45.9 BIGR real
## 13 1032 8865 82.6496 7.124000 27.0 49.7 BIGR real
## 14 754 8876 82.7522 5.148700 26.1 48.4 BIGR real
## 15 1011 8529 79.5171 6.987600 27.0 48.9 BIGR real
## 16 1634 9201 85.7822 11.253800 26.9 50.7 BIGR real
## 17 1057 8997 83.8803 7.211080 26.1 49.2 BIGR real
## 18 1222 9065 84.5143 8.391200 26.2 50.3 BIGR real
## 19 1445 8993 83.8430 9.992910 26.9 49.1 BIGR real
## 20 1019 8866 82.6590 6.969700 26.7 50.8 BIGR real
## 21 1059 8436 78.6500 7.282490 26.3 49.1 BITJ real
## 22 324 7743 72.1891 2.151030 22.6 46.9 BK70 real
## 23 1782 9830 91.6465 12.451600 23.0 51.0 BKW2 real
## 24 2797 10047 93.6696 19.518800 27.6 53.0 BLAU real
## 25 575 8523 79.4611 3.958230 27.8 47.7 BNMA real
## 26 951 9828 91.6278 6.660080 27.4 49.9 BNMA real
## 27 690 8638 80.5333 4.793490 27.5 49.3 BNMA real
## 28 476 8092 75.4428 3.286410 27.4 47.7 BNMA real
## 29 514 7970 74.3054 3.520600 27.4 48.0 BNMA real
## 30 1168 9970 92.9517 8.158310 26.6 52.3 BNST real
## 31 1073 10021 93.4272 7.537670 26.8 52.6 BNST real
## 32 1108 9847 91.8050 7.776150 26.7 51.9 BNST real
## 33 975 9607 89.5674 6.808410 26.9 52.1 BNST real
## 34 881 8900 82.9759 6.045030 26.9 49.8 BNST real
## 35 1674 9765 91.0405 11.714600 27.7 52.7 BOOT real
## 36 2238 9804 91.4041 15.621200 24.2 50.9 BOUL real
## 37 1895 9863 91.9541 13.228000 23.9 51.1 BOUL real
## 38 1916 9856 91.8889 13.280600 26.1 52.2 BRLY real
## 39 1950 9872 92.0380 13.632900 24.0 51.6 BRNT real
## 40 647 7433 69.2989 4.391390 27.9 48.6 BSEA real
## 41 1301 9011 84.0108 8.970170 26.2 49.7 CERC real
## 42 1131 9702 90.4531 7.903690 27.1 52.5 CHRU real
## 43 1499 9813 91.4880 10.449900 27.9 52.7 CMCB real
## 44 596 9189 85.6703 4.152340 27.6 51.7 COAT real
## 45 1414 9532 88.8682 9.785570 25.4 51.9 COND real
## 46 1754 9693 90.3692 12.248600 28.0 52.9 CORC real
## 47 1219 10002 93.2500 8.508020 27.5 52.9 DAIM real
## 48 1535 9863 91.9541 10.739200 27.8 52.5 DANS real
## 49 1033 8658 80.7197 7.135740 23.8 45.1 DARW real
## 50 1160 8935 83.3023 7.978000 23.6 46.0 DAWS real
## 51 1773 9937 92.6440 12.422000 27.7 52.4 DNSE real
## 52 2614 9872 92.0380 18.237000 23.5 51.2 DRIZ real
## 53 2752 10064 93.8281 19.223700 24.1 51.9 DRIZ real
## 54 4166 10206 95.1520 29.156200 23.7 51.0 DRIZ real
## 55 2731 9915 92.4389 19.051000 23.5 51.1 DRIZ real
## 56 2313 10179 94.9002 16.210800 27.7 52.3 ECHO real
## 57 969 9320 86.8917 6.726650 24.0 50.7 EDEN real
## 58 1293 8508 79.3213 8.877490 26.2 50.8 ERBC real
## 59 1514 9053 84.4024 10.440900 24.7 54.2 ESCP real
## 60 1050 9053 84.4024 7.270180 23.3 44.5 FADA real
## 61 1950 9238 86.1272 13.458100 21.9 49.1 FRIC real
## 62 1259 8728 81.3724 8.610010 24.9 51.1 FRIL real
## 63 1579 6514 60.7309 5.266920 29.5 49.6 FTC real
## 64 1318 8707 81.1766 9.043260 26.1 49.9 GARC real
## 65 599 8394 78.2584 4.097050 23.2 47.1 GIFU real
## 66 291 4057 37.8240 0.965691 30.1 44.4 GJOG real
## 67 3506 9983 93.0729 24.463700 23.3 51.1 GOLD real
## 68 1030 9935 92.6254 7.218530 27.3 53.4 GOLD real
## 69 266 4124 38.4486 0.885978 30.8 45.0 GORT real
## 70 2537 10058 93.7721 17.686900 26.7 51.9 HRUN real
## 71 602 8702 81.1300 4.134530 27.3 48.7 HSTA real
## 72 1206 6451 60.1436 4.023030 27.7 48.9 HUTU real
## 73 1927 9967 92.9237 13.518600 27.9 52.7 JADE real
## 74 326 4046 37.7214 1.080550 30.0 46.1 JAMA real
## 75 566 8454 78.8178 3.857080 27.2 48.2 KALD real
## 76 1870 9956 92.8212 13.130500 28.0 52.5 KFSY real
## 77 647 8310 77.4753 4.415810 26.9 46.6 KODK real
## 78 584 8381 78.1372 4.054630 26.8 49.4 LAMH real
## 79 684 8500 79.2467 4.714340 26.5 46.4 LANO real
## 80 1969 9309 86.7891 13.487400 24.9 51.8 LAUR real
## 81 585 8827 82.2954 3.995250 22.7 47.2 LITC real
## 82 588 8339 77.7457 3.988160 21.8 44.4 LITC real
## 83 348 6862 63.9754 2.350830 22.6 46.8 LITC real
## 84 602 7902 73.6715 3.995710 21.9 46.3 LITC real
## 85 862 8880 82.7895 5.966620 23.2 47.2 LITC real
## 86 1571 9717 90.5930 10.735900 22.2 50.7 LITC real
## 87 1250 9496 88.5325 8.629870 22.0 50.3 LITC real
## 88 1640 9480 88.3834 10.563800 21.5 48.4 LITC real
## 89 2083 9807 91.4320 14.445600 22.8 50.9 LITC real
## 90 1185 9434 87.9545 8.010630 22.0 49.9 LITC real
## 91 669 8385 78.1745 4.643480 27.5 48.3 LLYD real
## 92 1831 9963 92.8864 12.765300 27.7 52.6 LMCK real
## 93 1361 9395 87.5909 9.462610 25.3 52.3 LMIA real
## 94 902 8787 81.9224 6.177610 27.1 47.6 LMOR real
## 95 2383 9986 93.1009 16.629100 26.6 52.5 LNEA real
## 96 735 9657 90.0336 5.145350 27.3 51.9 LNGH real
## 97 1322 9668 90.1361 9.233920 27.4 50.5 LOBG real
## 98 1462 9641 89.8844 10.084300 26.2 51.5 LOBG real
## 99 2651 9809 91.4507 18.479500 26.2 52.4 LOBG real
## 100 1632 9701 90.4438 11.330200 26.3 51.3 LOBG real
## 101 803 8049 75.0420 5.517810 26.2 49.6 LOBG real
## 102 1363 9622 89.7073 9.395860 26.4 51.2 LOBG real
## 103 2276 9914 92.4296 15.827700 26.3 52.3 LOBG real
## 104 2308 9924 92.5228 16.054000 26.2 51.6 LOBG real
## 105 1297 9591 89.4182 8.998510 26.0 51.0 LOBG real
## 106 760 8791 81.9597 5.207720 26.7 47.7 LOBG real
## 107 1224 9712 90.5463 8.455900 26.4 51.8 LOBG real
## 108 1377 9839 91.7304 9.562190 26.3 51.9 LOBG real
## 109 1458 9990 93.1382 10.102000 26.5 50.7 LOBG real
## 110 1387 9952 92.7839 9.674340 26.4 50.4 LOBG real
## 111 1833 10084 94.0145 12.767300 26.8 52.0 LOBG real
## 112 1198 9585 89.3623 8.302260 26.7 50.7 LOBG real
## 113 1509 9686 90.3039 10.448400 26.5 51.0 LOBG real
## 114 831 8750 81.5775 5.728790 26.4 48.1 LOBG real
## 115 2061 10143 94.5646 14.336800 26.3 52.3 LOBG real
## 116 1950 10240 95.4690 13.624700 26.4 52.2 LOBG real
## 117 1863 10011 93.3340 13.025900 27.8 51.0 LONG real
## 118 1470 10006 93.2873 10.279000 27.8 52.8 LSHP real
## 119 1107 9052 84.3931 7.566100 24.9 49.8 LSOL real
## 120 563 8411 78.4169 3.827710 27.2 47.0 LSWL real
## 121 672 9024 84.1320 4.667350 27.0 45.9 LTLT real
## 122 1196 9530 88.8495 8.330690 26.2 52.3 LUTE real
## 123 745 8905 83.0226 5.144880 27.3 48.2 MANC real
## 124 343 4258 39.6979 1.142740 29.1 49.0 MATA real
## 125 2343 9963 92.8864 16.349200 23.9 50.6 MAYR real
## 126 2881 9767 91.0591 20.092600 23.3 51.4 MAYR real
## 127 2094 9728 90.6955 14.550800 25.7 53.0 MAYR real
## 128 2006 9744 90.8447 13.922200 25.7 53.1 MAYR real
## 129 1920 9908 92.3737 13.287600 25.9 52.4 MAYR real
## 130 1730 9948 92.7466 12.104500 26.8 52.1 MAYR real
## 131 2869 9597 89.4742 19.853000 24.7 54.0 MAYR real
## 132 3803 9387 87.5163 26.089000 24.0 54.6 MAYR real
## 133 2627 9888 92.1872 18.236200 25.6 53.5 MAYR real
## 134 1486 9651 89.9776 10.295800 25.7 52.7 MAYR real
## 135 2065 9640 89.8751 14.451900 25.5 51.9 MAYR real
## 136 2397 9546 88.9987 16.569100 25.0 53.6 MAYR real
## 137 831 8683 80.9528 5.717320 23.3 45.1 MDPC real
## 138 1207 9084 84.6914 8.274380 26.3 48.3 MIDF real
## 139 909 8716 81.2605 6.267670 26.1 48.8 MIDF real
## 140 936 8421 78.5102 6.429610 26.3 47.6 MIDF real
## 141 2220 10276 95.8046 15.502600 26.5 51.7 MIDF real
## 142 2175 10115 94.3036 15.172100 26.2 52.2 MIDF real
## 143 1794 10010 93.3246 12.447800 25.8 51.8 MIDF real
## 144 2221 10125 94.3968 15.385200 25.7 51.8 MIDF real
## 145 2308 9969 92.9424 16.083300 26.4 52.2 MIDF real
## 146 1181 8517 79.4052 8.056400 26.6 47.9 MIDF real
## 147 674 8799 82.0343 4.674720 26.3 47.6 MIDF real
## 148 1154 9851 91.8423 8.063680 27.6 51.9 MNKA real
## 149 1721 9926 92.5415 12.036500 27.7 51.7 MNYN real
## 150 831 6291 58.6519 2.758060 30.5 48.8 MUDL real
## 151 240 968 9.0248 0.801137 28.7 43.4 NEU real
## 152 680 8479 79.0509 4.662220 27.7 48.2 NHBR real
## 153 573 4571 42.6161 1.903970 29.8 46.4 NOST real
## 154 829 7241 67.5089 5.704270 25.9 45.2 NTRW real
## 155 548 8116 75.6666 3.804960 27.3 47.1 OBSE real
## 156 462 7475 69.6905 3.051460 20.4 47.1 OLNY real
## 157 1314 9906 92.3550 9.190010 27.6 51.8 ORPH real
## 158 1109 6343 59.1367 3.694010 29.6 49.4 PAXB real
## 159 1122 8821 82.2394 7.736900 26.3 50.8 PINC real
## 160 543 8364 77.9787 3.747530 27.2 47.3 POQU real
## 161 835 8873 82.7242 5.626980 20.2 51.6 QUIN real
## 162 372 4787 44.6299 1.621290 28.5 41.5 RABS real
## 163 1831 9710 90.5277 12.834600 26.6 52.0 RCAL real
## 164 2290 9955 92.8119 15.959500 23.4 50.3 RDSP real
## 165 2300 9885 92.1592 15.863100 26.5 51.9 RDSP real
## 166 1591 9532 88.8682 10.897800 26.1 53.0 RDSP real
## 167 1799 9999 93.2221 12.413900 26.6 52.2 RDSP real
## 168 1997 9473 88.3181 13.830500 26.4 52.8 RDSP real
## 169 1918 9890 92.2059 13.193100 26.1 52.2 RDSP real
## 170 1624 9714 90.5650 11.286700 26.4 52.5 RDSP real
## 171 1566 9684 90.2853 10.777600 26.4 52.3 RDSP real
## 172 1472 9383 87.4790 10.080000 26.0 53.4 RDSP real
## 173 1777 9798 91.3481 12.265800 26.2 52.8 RDSP real
## 174 1358 9569 89.2131 9.345610 26.3 51.8 RDSP real
## 175 1087 9517 88.7283 7.569640 26.7 50.6 REIV real
## 176 1168 9700 90.4345 8.196250 27.4 52.0 RGLN real
## 177 2332 9914 92.4296 16.129500 24.6 52.8 ROUG real
## 178 1452 8871 82.7056 9.953380 26.1 50.5 SACK real
## 179 151 3027 28.2211 0.499627 29.7 45.4 SALR real
## 180 1765 8993 83.8430 12.146600 25.8 50.9 SALS real
## 181 1087 8903 83.0039 7.464390 26.1 50.3 SAPC real
## 182 1269 8830 82.3233 8.721420 26.4 50.5 SCRM real
## 183 1392 8753 81.6054 9.640590 26.6 50.7 SCRS real
## 184 601 5994 55.8829 1.996080 30.8 49.5 SCX real
## 185 1177 9471 88.2995 8.165300 22.5 52.0 SDPY real
## 186 930 6015 56.0787 3.087640 30.7 50.1 SHEL real
## 187 1622 9173 85.5212 10.849000 20.9 49.1 SHEL real
## 188 1177 9260 86.3323 8.147680 26.6 49.0 SJCR real
## 189 2805 10083 94.0052 19.589300 23.3 50.7 SKID real
## 190 2011 9873 92.0474 14.053800 27.6 52.3 SKON real
## 191 1252 8415 78.4542 8.588480 23.9 52.1 SLMW real
## 192 2145 10039 93.5950 15.029900 27.8 51.7 SLTC real
## 193 1192 9621 89.6979 8.332840 26.8 53.5 SLVR real
## 194 2045 9969 92.9424 14.202900 23.4 49.8 SPNC real
## 195 1031 8624 80.4028 7.125210 23.3 46.8 SPNC real
## 196 2500 9852 91.8516 17.480000 23.2 51.5 SPNC real
## 197 3269 9929 92.5695 22.779700 23.2 51.4 SPNC real
## 198 1709 10051 93.7069 11.925900 27.7 52.3 SRLY real
## 199 1494 9507 88.6351 10.481700 26.3 52.3 SRST real
## 200 1467 9016 84.0574 9.990300 25.4 50.2 SSMC real
## 201 3015 10235 95.4223 21.003700 23.6 51.8 STIU real
## 202 1917 10116 94.3129 13.453800 27.7 52.4 STMY real
## 203 1206 9086 84.7101 8.258530 26.0 50.8 SUNC real
## 204 1699 9821 91.5626 11.876200 27.8 52.6 TERN real
## 205 721 8886 82.8454 4.968950 27.1 47.4 TORM real
## 206 306 4694 43.7628 1.015850 29.3 49.8 TYNE real
## 207 1029 8701 81.1206 7.162320 23.3 45.9 TYNE real
## 208 1032 8362 77.9601 7.149260 22.9 46.1 TYNE real
## 209 1040 8869 82.6869 7.154480 23.3 46.0 TYNE real
## 210 1610 9649 89.9590 11.176200 23.2 50.4 TYNE real
## 211 1049 8391 78.2305 7.274380 23.3 46.6 TYNE real
## 212 876 6318 58.9036 2.918240 30.4 48.4 TYNE real
## 213 1767 9917 92.4576 12.290000 23.5 51.1 TYNE real
## 214 2974 9905 92.3457 20.756400 22.4 51.4 TYNE real
## 215 2334 9764 91.0311 16.267400 23.2 51.0 TYNE real
## 216 2191 10007 93.2967 15.238400 23.1 51.1 TYNE real
## 217 2139 9891 92.2152 14.920900 23.0 50.9 TYNE real
## 218 2066 9706 90.4904 14.412100 26.5 52.4 URRI real
## 219 1988 9841 91.7490 13.873300 26.9 52.4 VIFC real
## 220 2697 9537 88.9148 18.640300 25.2 54.0 VIFR real
## 221 1715 9884 92.1499 11.986300 27.4 52.7 WALC real
## 222 1423 9689 90.3319 9.987320 27.3 52.6 WALR real
## 223 1304 9268 86.4069 9.104610 23.5 51.4 WATT real
## 224 1366 9878 92.0940 9.557710 23.3 51.3 WDPL real
## 225 441 7512 70.0354 2.937350 22.2 45.0 WMSO real
## 226 1753 9849 91.8236 12.170800 27.6 52.6 WOLF real
## 227 2126 10062 93.8094 14.920100 28.1 52.1 ZERO real
## weighted_mean_depth pop
## 1 5.6593572 Alaska Marine
## 2 6.1191022 Alaska Stream
## 3 10.2665175 Anser Lake, Haida Gwaii
## 4 0.7332322 Antigonish Landing, Nova Scotia, Marine
## 5 5.7129707 Barrow, Alaska
## 6 1.4560270 Bodega Bay
## 7 1.2070651 Bear Paw
## 8 5.2068374 North Uist, Scotland, Andrew Maccoll 21 May 2011
## 9 2.3439186 Big Lake (F)
## 10 1.6050164 Big River (M)
## 11 7.4781709 Big River, CA
## 12 7.5244250 Big River, CA
## 13 5.1880784 Big River, CA
## 14 6.3998250 Big River, CA
## 15 5.1195717 Big River, CA
## 16 5.2521757 Big River, CA
## 17 5.0344703 Big River, CA
## 18 5.8887788 Big River, CA
## 19 5.7324977 Big River, CA
## 20 4.8591415 Big River, CA
## 21 6.8958966 Bitrufjordur
## 22 9.0072574 Banks West, lake, Haida Gwaii
## 23 9.6392857 Banks 70, lake, Haida Gwaii
## 24 5.7998707 Blautaver Lake, Iceland
## 25 5.3101227 Bonsall marine
## 26 5.8571863 Bonsall marine
## 27 4.5056283 Bonsall marine
## 28 4.7503363 Bonsall marine
## 29 4.6754872 Bonsall marine
## 30 5.1243798 Bonsall stream
## 31 4.9778562 Bonsall stream
## 32 5.1286995 Bonsall stream
## 33 4.3786743 Bonsall stream
## 34 5.3279879 Bonsall stream
## 35 5.0370307 Boot Lake, Alaska
## 36 11.0161867 Boulton Lake, haida Gwaii
## 37 9.3903520 Boulton Lake, haida Gwaii
## 38 5.6745386 Barley Lake
## 39 10.2002365 Branta Lake, Haida Gwaii
## 40 6.8064430 Bering Sea
## 41 5.6196985 Cerrito Creek, Northern California
## 42 4.9029401 North Uist, Scotland CHRU Andrew Maccoll 20 May 2011
## 43 4.7270258 Community Club
## 44 5.1469960 Coates Lake, Haida Gwaii
## 45 5.6082479 Connor Creek D, Washington
## 46 5.6744946 Corcoran Lake, Alaska
## 47 5.2588930 North Uist, DAIM Andrew Maccoll 24 May 2011
## 48 4.6453401 Daniels Lake, Alaska
## 49 9.3617612 Darwin Lake, Haida Gwaii
## 50 11.1274830 Dawson Marine Pond, Haida Gwai
## 51 5.5763697 Denise Lake, Alaska
## 52 12.9269793 Drizzle Lake, inlet, Haida Gwaii
## 53 11.4626779 Drizzle Lake, Haida Gwaii
## 54 12.0242899 Drizzle Lake, Haida Gwaii
## 55 11.0507904 Drizzle Lake, outlet, Haida Gwaii
## 56 6.7741298 Echo Lake, Alaska
## 57 11.3075050 Eden Lake, Haida Gwaii
## 58 6.1012959 El Rosario Boca, Mexico
## 59 6.3509442 Escarpment Lake, Haida Gwaii
## 60 11.1778262 North Uist, Loch Fada
## 61 9.6235900 Friant River
## 62 5.9892293 Friant River
## 63 2.3527355 Fish Trap Creek, Washington
## 64 5.8876132 Garrity Creek, Northern California
## 65 5.4648449 Japanese Marine
## 66 1.8685515 Gjogur, Iceland, Marine
## 67 13.6788189 Gold Creek, Haida Gwaii
## 68 4.9021005 Gold Creek, Haida Gwaii
## 69 1.7040520 Gorten Sands, Scotland, Marine
## 70 5.9692801 Hraun, Iceland
## 71 5.3297352 North Uist, Scotland HOSTA Andrew Maccoll 18 May 2011
## 72 2.9365968 Humptulips, Washington, Freshwater
## 73 5.9535365 Jade Lake, Alaska
## 74 2.2288292 Japanese Marine
## 75 5.1999993 Kaldback, Faroe Islands
## 76 5.4475475 Kalifonsky Lake
## 77 6.0437283 Pacific Ocean offshore Kodiak Island
## 78 5.0899309 Loch a'Mhuilin
## 79 5.8646859 Loch an Nostarie
## 80 8.4144698 Laurel Pond, Haida Gwaii
## 81 7.4768327 Little Campbell downstream
## 82 8.4862530 Little Campbell downstream
## 83 7.1240731 Little Campbell downstream
## 84 8.3245718 Little Campbell downstream
## 85 9.2334751 Little Campbell downstream
## 86 8.8145202 Little Campbell upstream
## 87 8.7778775 Little Campbell upstream
## 88 9.8111686 Little Campbell upstream
## 89 10.6394774 Little Campbell upstream
## 90 7.5975163 Little Campbell upstream
## 91 5.5302740 Lloyd State Park, MA
## 92 5.1824694 Little Meadow Creek, Alaska
## 93 6.7022353 Limia, Spain
## 94 7.9151401 Loch Morar
## 95 5.3970335 Loch nan Eala
## 96 4.7596208 Lough Neagh, Ballyronan Bay, Ireland
## 97 5.7292047 Loberg Lake, early
## 98 6.7115305 Loberg Lake, early
## 99 7.4320446 Loberg Lake, early
## 100 7.3085139 Loberg Lake, early
## 101 7.6706016 Loberg Lake, early
## 102 6.2234395 Loberg Lake, early
## 103 6.6593630 Loberg Lake, early
## 104 7.0157263 Loberg Lake, early
## 105 5.9657157 Loberg Lake, early
## 106 6.8911003 Loberg Lake, early
## 107 5.8502730 Loberg Lake, later
## 108 6.1697366 Loberg Lake, later
## 109 6.0957819 Loberg Lake, later
## 110 6.1045093 Loberg Lake, later
## 111 5.5668464 Loberg Lake, later
## 112 5.7840358 Loberg Lake, later
## 113 6.8838001 Loberg Lake, later
## 114 7.1986307 Loberg Lake, later
## 115 5.9638877 Loberg Lake, later
## 116 5.5151988 Loberg Lake, later
## 117 5.9466847 Long Lake, Alaska
## 118 5.3319020 L-shaped lake
## 119 6.7036664 Lake Solano, CA
## 120 4.9910335 Lough Swilly, Co Donegal
## 121 5.7242339 Lough Talt, Co Sligo
## 122 6.0352348 Lutea Lake, Haida Gwaii
## 123 6.4361687 Manchester Clam Bay, Puget Sound
## 124 2.0881693 Matadero
## 125 11.8436738 Mayer Lake
## 126 8.2861903 Mayer Lake
## 127 5.9841121 Mayer Lake
## 128 6.8030007 Mayer Lake
## 129 5.6622857 Mayer Lake
## 130 5.0628956 Mayer Lake
## 131 7.8243280 Mayer Lake
## 132 9.0175331 Mayer Lake
## 133 7.3820319 Mayer Lake
## 134 6.2882460 Mayer Lake
## 135 6.9276230 Mayer Lake
## 136 8.0912364 Mayer Lake
## 137 10.3976967 Mid-pacific, off Haida Gwaii
## 138 6.9000706 Midfjardara River Downstream
## 139 5.5948720 Midfjardara River Downstream
## 140 6.1825375 Midfjardara River Downstream
## 141 7.3160002 Midfjardara River Upstream
## 142 5.3980437 Midfjardara River Upstream
## 143 6.2871038 Midfjardara River Upstream
## 144 6.8262899 Midfjardara River Upstream
## 145 6.4462492 Midfjardara River Upstream
## 146 7.1926416 Midfjardara River Downstream
## 147 5.8657823 Midfjardara River Downstream
## 148 5.1867059 Matanuska Lake, Alaska
## 149 5.2552549 Menyanthes Lake, Haida Gwai
## 150 1.4220406 Mud Lake
## 151 1.5902901 Neustadt, Germany, Marine
## 152 6.0468999 New Harbor River, US East Coast
## 153 0.8447451 Norway Stream, Freshwater
## 154 6.7714109 Norway Trawler
## 155 5.5536763 Scotland, OBSE+OBSM Andrew Maccoll
## 156 8.3406836 Olney Creek, California
## 157 4.5714907 Orphea Lake, Alaska
## 158 1.9925720 Paxton Benthic
## 159 5.0384202 Pinole Creek, Northern California
## 160 5.1586238 Poque Lake, Haida Gwaii
## 161 6.7023087 Quinalt, Washington
## 162 3.0829753 Rabbit Slough
## 163 5.8027280 River Callop, Scotland
## 164 11.0519227 Roadside Pond
## 165 7.6260500 Roadside Pond
## 166 6.3088523 Roadside Pond
## 167 5.9678947 Roadside Pond
## 168 6.6826979 Roadside Pond
## 169 6.9902077 Roadside Pond
## 170 5.6068552 Roadside Pond
## 171 6.2901291 Roadside Pond
## 172 5.0363602 Roadside Pond
## 173 6.8767427 Roadside Pond
## 174 7.1022094 Roadside Pond
## 175 5.1747957 North Uist, Scotland, Andrew Maccoll
## 176 5.3614152 River Glenavy, Co Antrim, Ireland
## 177 7.2687331 Rouge Lake, Haida Gwaii
## 178 5.8404062 San Antonio Creek, VAFB, CA
## 179 1.1687793 Salmon River, marine
## 180 6.6431733 Salinas River, CA
## 181 4.9594245 San Pablo Creek, Northern California
## 182 6.0176383 Santa Clara River mouth, LA, CA
## 183 6.5637238 Santa Clara River, LA, CA
## 184 1.1804410 Schwalle River, Germany, freshwater
## 185 11.7550906 Serendipity Pond, Haida Gwaii
## 186 1.5803198 River Shiel, Scotland, freshwater
## 187 7.6669384 Loch Shiel
## 188 5.8217646 San Jacinto River, San Bernadino, CA
## 189 9.3390974 Skidgate Lake, Haida Gwaii
## 190 6.2545067 Skonun Lake, Haida Gwaii
## 191 5.7520814 Sugarloaf Meadow, San Bernadino, CA
## 192 6.1719665 Solstice Lake, Haida Gwaii
## 193 5.4704310 Silver Lake, Haida Gwaii
## 194 10.4703091 Spence Lake, Haida Gwaii
## 195 10.8371614 Spence Lake, Haida Gwaii
## 196 10.7987807 Spence Lake Outlet, Haida Gwaii
## 197 11.0222917 Spence Lake Outlet, Haida Gwaii
## 198 5.1617288 South Rolly Lake, Alaska
## 199 5.5126936 Salmon River Stream, British Columbia
## 200 6.8748735 San Simeon Creek, CA
## 201 9.8822717 Stiu Lake, Queen Charlotte - fully armored fresh
## 202 5.6529617 Stormy Lake, Alaska
## 203 5.2009857 Suisun Creek, Northern California
## 204 5.2308375 Tern Lake, Alaska
## 205 6.4907516 Torm Lake, Scotland
## 206 0.9806016 Tyne downstream
## 207 10.3758825 Tyne River marine
## 208 10.2345778 Tyne River marine
## 209 10.1085230 Tyne River marine
## 210 8.0013988 Tyne River marine
## 211 10.6449995 Tyne River marine
## 212 2.2853489 Tyne upstream
## 213 8.5327233 Tyne River upstream
## 214 14.3031165 Tyne River upstream
## 215 10.8699134 Tyne River upstream
## 216 10.1857972 Tyne River upstream
## 217 10.2538999 Tyne River upstream
## 218 6.3337425 Urridakotsvatn
## 219 5.8010832 Vifissta_avatn
## 220 7.8636075 Vifissta_avatn
## 221 6.2753807 Wallace Lake
## 222 5.7690872 Wallace Lake
## 223 6.6066058 Watt Lake, Haida Gwaii
## 224 5.2269304 Woodpile Creek, Haida gwai
## 225 7.1172362 Williamsoni, CA; no plates
## 226 5.5945258 Wolf Lake, Alaska
## 227 4.9839896 Zero Lake, Alaska
## coord_approx GPS_north GPS_east mar_fresh notes water_type
## 1 <NA> 60.124 -149.395 M <NA> Marine
## 2 <NA> 60.141 -149.395 F <NA> River
## 3 <NA> 54.114 -131.686 F <NA> Freshwater
## 4 <NA> 45.698 -61.878 M Jones et al. 2012 Marine
## 5 <NA> 71.335 -156.606 M <NA> Marine
## 6 <NA> 38.325 -123.041 M Jones et al. 2012 Marine
## 7 <NA> 61.615 -149.757 F Jones et al. 2012 Fresh
## 8 <NA> 57.567 -7.283 F <NA> Lake
## 9 <NA> 39.300 -123.728 F Jones et al. 2012 Fresh
## 10 <NA> 39.289 -123.747 M Jones et al. 2012 Marine
## 11 <NA> 39.289 -123.747 M <NA> Marine
## 12 <NA> 39.317 -123.686 F <NA> River
## 13 <NA> 39.289 -123.747 M <NA> Marine
## 14 <NA> 39.289 -123.747 M <NA> Marine
## 15 <NA> 39.289 -123.747 M <NA> Marine
## 16 <NA> 39.289 -123.747 M <NA> Marine
## 17 <NA> 39.317 -123.686 F <NA> River
## 18 <NA> 39.317 -123.686 F <NA> River
## 19 <NA> 39.317 -123.686 F <NA> River
## 20 <NA> 39.317 -123.686 F <NA> River
## 21 <NA> 65.457 -21.440 M <NA> Marine
## 22 <NA> 53.397 -130.202 F <NA> Freshwater
## 23 <NA> 53.359 -130.157 F <NA> Freshwater
## 24 <NA> 64.044 -18.991 F <NA> Lake
## 25 <NA> 48.885 -123.667 M <NA> Marine
## 26 <NA> 48.885 -123.667 M <NA> Marine
## 27 <NA> 48.885 -123.667 M <NA> Marine
## 28 <NA> 48.885 -123.667 M <NA> Marine
## 29 <NA> 48.885 -123.667 M <NA> Marine
## 30 <NA> 48.850 -123.723 F <NA> River
## 31 <NA> 48.850 -123.723 F <NA> River
## 32 <NA> 48.850 -123.723 F <NA> River
## 33 <NA> 48.850 -123.723 F <NA> River
## 34 <NA> 48.850 -123.723 F <NA> River
## 35 <NA> 61.718 -150.130 F <NA> Lake
## 36 <NA> 53.783 -132.098 F <NA> Lake
## 37 <NA> 53.783 -132.098 F <NA> Lake
## 38 <NA> 61.363 -150.083 F <NA> Lake
## 39 <NA> 53.967 -132.016 F <NA> Freshwater
## 40 Y 58.089 -166.643 M <NA> Marine
## 41 <NA> 37.901 -122.281 F <NA> Freshwater
## 42 <NA> 57.600 -7.200 F <NA> Fresh
## 43 <NA> 60.702 -151.383 F <NA> Fresh
## 44 <NA> 53.669 -132.880 F <NA> Fresh
## 45 <NA> 47.056 -124.163 F <NA> River
## 46 <NA> 61.574 -149.688 F <NA> Lake
## 47 <NA> 57.583 -7.200 F <NA> Fresh
## 48 <NA> 60.735 -151.186 F <NA> Lake
## 49 <NA> 52.568 -131.663 F <NA> Lake
## 50 <NA> 53.225 -132.476 M <NA> Marine
## 51 <NA> 60.522 -150.987 F <NA> Lake
## 52 <NA> 53.925 -132.077 F <NA> Lake
## 53 <NA> 53.935 -132.073 F <NA> Lake
## 54 <NA> 53.935 -132.073 F <NA> Lake
## 55 <NA> 53.934 -132.053 F <NA> Lake
## 56 <NA> 60.438 -151.161 F <NA> Lake
## 57 <NA> 53.845 -132.746 F <NA> Lake
## 58 Y 30.036 -115.774 F <NA> <NA>
## 59 <NA> 52.589 -131.842 F <NA> Lake
## 60 <NA> 57.613 -7.211 F <NA> Lake
## 61 <NA> 36.980 -119.731 F Completely plated River
## 62 <NA> 36.980 -119.731 F Low plated River
## 63 <NA> 48.931 -122.487 F Jones et al. 2012 Fresh
## 64 <NA> 38.001 -122.322 F <NA> Creek
## 65 Y 35.405 136.701 M <NA> Marine
## 66 <NA> 65.980 -21.440 M Jones et al. 2012 Marine
## 67 <NA> 53.632 -132.053 F <NA> Fresh
## 68 <NA> 53.632 -132.053 F <NA> Fresh
## 69 <NA> 56.911 -5.888 M Jones et al. 2012 Marine
## 70 <NA> 64.917 -22.167 F <NA> <NA>
## 71 <NA> 57.617 -7.483 F <NA> fresh
## 72 <NA> 47.231 -123.957 F Jones et al. 2012 Fresh
## 73 <NA> 61.524 -149.869 F <NA> Lake
## 74 <NA> 42.973 144.329 M Jones et al. 2012 Marine
## 75 <NA> 62.064 -6.913 M <NA> Marine
## 76 <NA> 60.331 -151.264 F <NA> Lake
## 77 Y 56.965 -151.350 M <NA> Marine
## 78 <NA> 56.894 -5.869 F <NA> Lake
## 79 <NA> 56.993 -5.804 F <NA> Lake
## 80 <NA> 53.929 -132.020 F <NA> Lake
## 81 <NA> 49.013 -122.779 M <NA> Marine
## 82 <NA> 49.013 -122.779 M <NA> Marine
## 83 <NA> 49.013 -122.779 M <NA> Marine
## 84 <NA> 49.013 -122.779 M <NA> Marine
## 85 <NA> 49.013 -122.779 M <NA> Marine
## 86 <NA> 49.012 -122.625 F <NA> River
## 87 <NA> 49.012 -122.625 F <NA> River
## 88 <NA> 49.012 -122.625 F <NA> River
## 89 <NA> 49.012 -122.625 F <NA> River
## 90 <NA> 49.012 -122.625 F <NA> River
## 91 <NA> 41.530 -70.981 M <NA> Marine
## 92 <NA> 61.569 -149.761 F <NA> River
## 93 <NA> 41.910 -8.093 F <NA> River
## 94 <NA> 57.567 -7.267 F <NA> Lake
## 95 <NA> 56.906 -5.833 F <NA> Lake
## 96 <NA> 54.708 -6.528 F <NA> Very Large Lake
## 97 <NA> 61.560 -149.258 F <NA> Lake
## 98 <NA> 61.560 -149.258 F <NA> Lake
## 99 <NA> 61.560 -149.258 F <NA> Lake
## 100 <NA> 61.560 -149.258 F <NA> Lake
## 101 <NA> 61.560 -149.258 F <NA> Lake
## 102 <NA> 61.560 -149.258 F <NA> Lake
## 103 <NA> 61.560 -149.258 F <NA> Lake
## 104 <NA> 61.560 -149.258 F <NA> Lake
## 105 <NA> 61.560 -149.258 F <NA> Lake
## 106 <NA> 61.560 -149.258 F <NA> Lake
## 107 <NA> 61.560 -149.258 F <NA> Lake
## 108 <NA> 61.560 -149.258 F <NA> Lake
## 109 <NA> 61.560 -149.258 F <NA> Lake
## 110 <NA> 61.560 -149.258 F <NA> Lake
## 111 <NA> 61.560 -149.258 F <NA> Lake
## 112 <NA> 61.560 -149.258 F <NA> Lake
## 113 <NA> 61.560 -149.258 F <NA> Lake
## 114 <NA> 61.560 -149.258 F <NA> Lake
## 115 <NA> 61.560 -149.258 F <NA> Lake
## 116 <NA> 61.560 -149.258 F <NA> Lake
## 117 <NA> 61.578 -149.764 F <NA> Lake
## 118 <NA> 61.706 -149.972 F <NA> Lake
## 119 <NA> 38.495 -122.033 F <NA> Lake
## 120 <NA> 55.016 -7.573 M <NA> Marine
## 121 <NA> 54.082 -8.920 F <NA> Lake
## 122 <NA> 52.337 -131.370 F <NA> Lake
## 123 <NA> 47.567 -122.547 M <NA> Marine
## 124 <NA> 37.393 -122.162 F Jones et al. 2012 Fresh
## 125 <NA> 53.692 -132.044 F <NA> Lake
## 126 <NA> 53.692 -132.044 F <NA> Lake
## 127 <NA> 53.692 -132.044 F <NA> Lake
## 128 <NA> 53.692 -132.044 F <NA> Lake
## 129 <NA> 53.692 -132.044 F <NA> Lake
## 130 <NA> 53.692 -132.044 F <NA> Lake
## 131 <NA> 53.692 -132.044 F <NA> Lake
## 132 <NA> 53.692 -132.044 F <NA> Lake
## 133 <NA> 53.692 -132.044 F <NA> Lake
## 134 <NA> 53.692 -132.044 F <NA> Lake
## 135 <NA> 53.692 -132.044 F <NA> Lake
## 136 <NA> 53.692 -132.044 F <NA> Lake
## 137 Y 53.000 -133.000 M <NA> Marine
## 138 <NA> 65.346 -20.911 M <NA> Marine
## 139 <NA> 65.346 -20.911 M <NA> Marine
## 140 <NA> 65.346 -20.911 M <NA> Marine
## 141 <NA> 65.037 -20.714 F <NA> River
## 142 <NA> 65.037 -20.714 F <NA> River
## 143 <NA> 65.037 -20.714 F <NA> River
## 144 <NA> 65.037 -20.714 F <NA> River
## 145 <NA> 65.037 -20.714 F <NA> River
## 146 <NA> 65.346 -20.911 M <NA> Marine
## 147 <NA> 65.346 -20.911 M <NA> Marine
## 148 <NA> 61.556 -149.229 F <NA> Lake
## 149 <NA> 53.596 -132.971 F <NA> Lake
## 150 <NA> 61.593 -149.340 F Jones et al. 2012 Lake
## 151 <NA> 54.058 10.877 M Jones et al. 2012 Marine
## 152 <NA> 44.473 -64.082 M <NA> Marine
## 153 <NA> 59.765 5.712 F Jones et al. 2012 Fresh
## 154 Y 68.500 2.000 M <NA> Marine
## 155 Y 57.614 -7.179 F <NA> <NA>
## 156 <NA> 40.528 -122.384 F <NA> River
## 157 <NA> 60.388 -151.199 F <NA> Lake
## 158 <NA> 49.703 -124.522 F Jones et al. 2012 Fresh
## 159 <NA> 37.963 -122.202 F <NA> Creek
## 160 <NA> 52.602 -131.704 F <NA> Lake
## 161 <NA> 47.471 -123.871 F <NA> Fresh
## 162 <NA> 61.556 -149.249 M Jones et al. 2012 Marine
## 163 <NA> 56.867 -5.425 F <NA> River
## 164 <NA> 53.624 -132.036 F <NA> Lake
## 165 <NA> 53.624 -132.036 F <NA> Lake
## 166 <NA> 53.624 -132.036 F <NA> Lake
## 167 <NA> 53.624 -132.036 F <NA> Lake
## 168 <NA> 53.624 -132.036 F <NA> Lake
## 169 <NA> 53.624 -132.036 F <NA> Lake
## 170 <NA> 53.624 -132.036 F <NA> Lake
## 171 <NA> 53.624 -132.036 F <NA> Lake
## 172 <NA> 53.624 -132.036 F <NA> Lake
## 173 <NA> 53.624 -132.036 F <NA> Lake
## 174 <NA> 53.624 -132.036 F <NA> Lake
## 175 <NA> 57.617 -7.517 F <NA> Lake
## 176 <NA> 54.588 -6.239 F <NA> River
## 177 <NA> 54.034 -131.876 F <NA> Lake
## 178 <NA> 34.783 -120.536 F <NA> River
## 179 <NA> 49.175 -122.594 M Jones et al. 2012 Marine
## 180 <NA> 36.647 -121.702 F <NA> River
## 181 <NA> 37.966 -122.320 F <NA> Creek
## 182 <NA> 34.236 -119.257 M <NA> Marine
## 183 Y 34.436 -118.612 F <NA> Fresh
## 184 <NA> 54.080 10.083 F Jones et al. 2012 Fresh
## 185 <NA> 54.028 -131.761 F <NA> Fresh
## 186 <NA> 56.748 -5.698 F Jones et al. 2012 Fresh
## 187 <NA> 56.787 -5.604 F <NA> Lake
## 188 Y 33.765 -117.208 F <NA> River
## 189 <NA> 53.097 -131.916 F <NA> Lake
## 190 <NA> 53.914 -132.025 F <NA> Lake
## 191 Y 34.179 -116.830 F <NA> Fresh
## 192 <NA> 53.945 -131.932 F <NA> Lake
## 193 <NA> 54.103 -131.688 F <NA> Lake
## 194 <NA> 53.973 -131.780 F <NA> Lake
## 195 <NA> 53.973 -131.780 F <NA> Lake
## 196 <NA> 53.982 -131.790 F <NA> Lake
## 197 <NA> 53.982 -131.790 F <NA> Lake
## 198 <NA> 61.669 -150.126 F <NA> Lake
## 199 <NA> 49.175 -122.594 F <NA> River
## 200 Y 35.608 -121.091 F <NA> River
## 201 <NA> 53.244 -132.586 F <NA> Lake
## 202 <NA> 60.771 -151.047 F <NA> Lake
## 203 Y 38.225 -122.107 F <NA> Creek
## 204 <NA> 60.533 -149.550 F <NA> Lake
## 205 <NA> 57.550 -7.317 F <NA> Lake
## 206 <NA> 55.999 -2.520 M Jones et al. 2012 Marine
## 207 <NA> 55.999 -2.520 M <NA> Marine
## 208 <NA> 55.999 -2.520 M <NA> Marine
## 209 <NA> 55.999 -2.520 M <NA> Marine
## 210 <NA> 55.999 -2.520 M <NA> Marine
## 211 <NA> 55.999 -2.520 M <NA> Marine
## 212 <NA> 55.943 -2.785 F Jones et al. 2012 Fresh
## 213 <NA> 55.943 -2.785 F <NA> River
## 214 <NA> 55.943 -2.785 F <NA> River
## 215 <NA> 55.943 -2.785 F <NA> River
## 216 <NA> 55.943 -2.785 F <NA> River
## 217 <NA> 55.943 -2.785 F <NA> River
## 218 <NA> 64.157 -21.343 F <NA> Fresh
## 219 <NA> 64.080 -21.873 F Pelvis Complete Fresh
## 220 <NA> 64.080 -21.873 F Pelvis Reduced Fresh
## 221 <NA> 61.573 -149.576 F Pelvis Complete Lake
## 222 <NA> 61.573 -149.576 F Pelvis Reduced Lake
## 223 <NA> 53.764 -132.071 F <NA> Lake
## 224 <NA> 53.644 -132.082 F <NA> Lake
## 225 <NA> 34.435 -118.198 F <NA> Fresh
## 226 <NA> 61.645 -149.278 F <NA> Lake
## 227 <NA> 61.648 -149.808 F <NA> Lake
## PNW_independent_MvsF_c150 NorthEurope_independent_MvsF_c151
## 1 1 NA
## 2 0 NA
## 3 0 NA
## 4 NA NA
## 5 1 NA
## 6 NA NA
## 7 0 NA
## 8 NA NA
## 9 NA NA
## 10 NA NA
## 11 NA NA
## 12 NA NA
## 13 NA NA
## 14 NA NA
## 15 NA NA
## 16 NA NA
## 17 NA NA
## 18 NA NA
## 19 NA NA
## 20 NA NA
## 21 NA 1
## 22 0 NA
## 23 0 NA
## 24 NA 0
## 25 1 NA
## 26 NA NA
## 27 NA NA
## 28 NA NA
## 29 NA NA
## 30 0 NA
## 31 NA NA
## 32 NA NA
## 33 NA NA
## 34 NA NA
## 35 0 NA
## 36 0 NA
## 37 NA NA
## 38 0 NA
## 39 0 NA
## 40 1 NA
## 41 NA NA
## 42 NA 0
## 43 0 NA
## 44 0 NA
## 45 0 NA
## 46 0 NA
## 47 NA NA
## 48 0 NA
## 49 0 NA
## 50 1 NA
## 51 0 NA
## 52 NA NA
## 53 0 NA
## 54 NA NA
## 55 NA NA
## 56 0 NA
## 57 0 NA
## 58 NA NA
## 59 0 NA
## 60 NA NA
## 61 NA NA
## 62 NA NA
## 63 0 NA
## 64 NA NA
## 65 NA NA
## 66 NA 1
## 67 0 NA
## 68 NA NA
## 69 NA 1
## 70 NA 0
## 71 NA NA
## 72 0 NA
## 73 0 NA
## 74 NA NA
## 75 NA 1
## 76 0 NA
## 77 1 NA
## 78 NA 0
## 79 NA 0
## 80 0 NA
## 81 1 NA
## 82 NA NA
## 83 NA NA
## 84 NA NA
## 85 NA NA
## 86 0 NA
## 87 NA NA
## 88 NA NA
## 89 NA NA
## 90 NA NA
## 91 NA NA
## 92 0 NA
## 93 NA NA
## 94 NA NA
## 95 NA 0
## 96 NA 0
## 97 NA NA
## 98 NA NA
## 99 NA NA
## 100 NA NA
## 101 NA NA
## 102 NA NA
## 103 NA NA
## 104 NA NA
## 105 NA NA
## 106 NA NA
## 107 NA NA
## 108 NA NA
## 109 NA NA
## 110 NA NA
## 111 NA NA
## 112 NA NA
## 113 NA NA
## 114 NA NA
## 115 NA NA
## 116 NA NA
## 117 0 NA
## 118 0 NA
## 119 NA NA
## 120 NA 1
## 121 NA 0
## 122 0 NA
## 123 1 NA
## 124 NA NA
## 125 0 NA
## 126 NA NA
## 127 NA NA
## 128 NA NA
## 129 NA NA
## 130 NA NA
## 131 NA NA
## 132 NA NA
## 133 NA NA
## 134 NA NA
## 135 NA NA
## 136 NA NA
## 137 1 NA
## 138 NA NA
## 139 NA NA
## 140 NA 1
## 141 NA 0
## 142 NA NA
## 143 NA NA
## 144 NA NA
## 145 NA NA
## 146 NA NA
## 147 NA NA
## 148 0 NA
## 149 0 NA
## 150 0 NA
## 151 NA 1
## 152 NA NA
## 153 NA 0
## 154 NA 1
## 155 NA NA
## 156 NA NA
## 157 0 NA
## 158 0 NA
## 159 NA NA
## 160 0 NA
## 161 0 NA
## 162 1 NA
## 163 NA 0
## 164 NA NA
## 165 NA NA
## 166 NA NA
## 167 NA NA
## 168 NA NA
## 169 NA NA
## 170 NA NA
## 171 NA NA
## 172 NA NA
## 173 NA NA
## 174 NA NA
## 175 NA 0
## 176 NA 0
## 177 0 NA
## 178 NA NA
## 179 1 NA
## 180 NA NA
## 181 NA NA
## 182 NA NA
## 183 NA NA
## 184 NA 0
## 185 0 NA
## 186 NA NA
## 187 NA 0
## 188 NA NA
## 189 0 NA
## 190 0 NA
## 191 NA NA
## 192 0 NA
## 193 0 NA
## 194 0 NA
## 195 NA NA
## 196 NA NA
## 197 NA NA
## 198 0 NA
## 199 0 NA
## 200 NA NA
## 201 0 NA
## 202 0 NA
## 203 NA NA
## 204 0 NA
## 205 NA NA
## 206 NA NA
## 207 NA NA
## 208 NA NA
## 209 NA NA
## 210 NA NA
## 211 NA 1
## 212 NA NA
## 213 NA 0
## 214 NA NA
## 215 NA NA
## 216 NA NA
## 217 NA NA
## 218 NA 0
## 219 NA NA
## 220 NA 0
## 221 NA NA
## 222 0 NA
## 223 0 NA
## 224 0 NA
## 225 NA NA
## 226 0 NA
## 227 0 NA
## CaliforniaFreshwater_vs_AllPacificMarine_c153 c154_globalsuperglacial_FvsM
## 1 1 1
## 2 NA NA
## 3 NA NA
## 4 NA 1
## 5 1 1
## 6 1 NA
## 7 NA 0
## 8 NA NA
## 9 NA NA
## 10 NA NA
## 11 1 NA
## 12 0 NA
## 13 NA NA
## 14 NA NA
## 15 NA NA
## 16 NA NA
## 17 NA NA
## 18 NA NA
## 19 NA NA
## 20 NA NA
## 21 NA 1
## 22 NA NA
## 23 NA NA
## 24 NA 0
## 25 1 1
## 26 NA NA
## 27 NA NA
## 28 NA NA
## 29 NA NA
## 30 NA 0
## 31 NA NA
## 32 NA NA
## 33 NA NA
## 34 NA NA
## 35 NA NA
## 36 NA 0
## 37 NA NA
## 38 NA NA
## 39 NA 0
## 40 1 1
## 41 0 NA
## 42 NA 0
## 43 NA NA
## 44 NA NA
## 45 NA NA
## 46 NA NA
## 47 NA NA
## 48 NA NA
## 49 NA NA
## 50 1 1
## 51 NA NA
## 52 NA NA
## 53 NA 0
## 54 NA NA
## 55 NA NA
## 56 NA NA
## 57 NA NA
## 58 0 NA
## 59 NA 0
## 60 NA NA
## 61 NA NA
## 62 0 NA
## 63 NA 0
## 64 0 NA
## 65 NA 1
## 66 NA 1
## 67 NA NA
## 68 NA NA
## 69 NA 1
## 70 NA 0
## 71 NA NA
## 72 NA NA
## 73 NA NA
## 74 NA 1
## 75 NA 1
## 76 NA 0
## 77 1 1
## 78 NA 0
## 79 NA 0
## 80 NA NA
## 81 1 1
## 82 NA NA
## 83 NA NA
## 84 NA NA
## 85 NA NA
## 86 NA 0
## 87 NA NA
## 88 NA NA
## 89 NA NA
## 90 NA NA
## 91 NA 1
## 92 NA NA
## 93 NA NA
## 94 NA NA
## 95 NA 0
## 96 NA 0
## 97 NA NA
## 98 NA NA
## 99 NA NA
## 100 NA NA
## 101 NA NA
## 102 NA NA
## 103 NA NA
## 104 NA NA
## 105 NA NA
## 106 NA NA
## 107 NA NA
## 108 NA NA
## 109 NA NA
## 110 NA NA
## 111 NA NA
## 112 NA NA
## 113 NA NA
## 114 NA NA
## 115 NA NA
## 116 NA NA
## 117 NA NA
## 118 NA 0
## 119 0 NA
## 120 NA 1
## 121 NA 0
## 122 NA NA
## 123 1 1
## 124 0 NA
## 125 NA 0
## 126 NA NA
## 127 NA NA
## 128 NA NA
## 129 NA NA
## 130 NA NA
## 131 NA NA
## 132 NA NA
## 133 NA NA
## 134 NA NA
## 135 NA NA
## 136 NA NA
## 137 1 1
## 138 NA NA
## 139 NA NA
## 140 NA 1
## 141 NA 0
## 142 NA NA
## 143 NA NA
## 144 NA NA
## 145 NA NA
## 146 NA NA
## 147 NA NA
## 148 NA 0
## 149 NA NA
## 150 NA NA
## 151 NA 1
## 152 NA 1
## 153 NA 0
## 154 1 1
## 155 NA NA
## 156 0 NA
## 157 NA 0
## 158 NA 0
## 159 0 NA
## 160 NA 0
## 161 NA 0
## 162 1 1
## 163 NA 0
## 164 NA NA
## 165 NA NA
## 166 NA NA
## 167 NA NA
## 168 NA NA
## 169 NA NA
## 170 NA NA
## 171 NA NA
## 172 NA NA
## 173 NA NA
## 174 NA NA
## 175 NA 0
## 176 NA 0
## 177 NA NA
## 178 0 NA
## 179 1 1
## 180 0 NA
## 181 0 NA
## 182 1 NA
## 183 0 NA
## 184 NA 0
## 185 NA NA
## 186 NA NA
## 187 NA 0
## 188 0 NA
## 189 NA NA
## 190 NA NA
## 191 0 NA
## 192 NA NA
## 193 NA NA
## 194 NA 0
## 195 NA NA
## 196 NA NA
## 197 NA NA
## 198 NA NA
## 199 NA 0
## 200 0 0
## 201 NA 0
## 202 NA NA
## 203 0 NA
## 204 NA NA
## 205 NA NA
## 206 NA NA
## 207 NA NA
## 208 NA NA
## 209 NA NA
## 210 NA NA
## 211 NA 1
## 212 NA NA
## 213 NA 0
## 214 NA NA
## 215 NA NA
## 216 NA NA
## 217 NA NA
## 218 NA 0
## 219 NA NA
## 220 NA 0
## 221 NA NA
## 222 NA NA
## 223 NA NA
## 224 NA NA
## 225 0 NA
## 226 NA NA
## 227 NA NA
## c155_global_FvsM used_joint_genotyping used _river_comparisons
## 1 1 Yes <NA>
## 2 NA Yes <NA>
## 3 NA Yes <NA>
## 4 1 Yes <NA>
## 5 1 Yes <NA>
## 6 1 Yes <NA>
## 7 0 Yes <NA>
## 8 NA Yes <NA>
## 9 NA Yes <NA>
## 10 NA Yes <NA>
## 11 1 Yes Yes
## 12 0 Yes Yes
## 13 NA Yes Yes
## 14 NA Yes Yes
## 15 NA Yes Yes
## 16 NA Yes Yes
## 17 NA Yes Yes
## 18 NA Yes Yes
## 19 NA Yes Yes
## 20 NA Yes Yes
## 21 1 Yes <NA>
## 22 NA Yes <NA>
## 23 NA Yes <NA>
## 24 0 Yes <NA>
## 25 1 Yes Yes
## 26 NA Yes Yes
## 27 NA Yes Yes
## 28 NA Yes Yes
## 29 NA Yes Yes
## 30 0 Yes Yes
## 31 NA Yes Yes
## 32 NA Yes Yes
## 33 NA Yes Yes
## 34 NA Yes Yes
## 35 NA Yes <NA>
## 36 0 Yes <NA>
## 37 NA Yes <NA>
## 38 NA Yes <NA>
## 39 0 Yes <NA>
## 40 1 Yes <NA>
## 41 0 Yes <NA>
## 42 0 Yes <NA>
## 43 NA Yes <NA>
## 44 NA Yes <NA>
## 45 NA Yes <NA>
## 46 NA Yes <NA>
## 47 NA Yes <NA>
## 48 NA Yes <NA>
## 49 NA Yes <NA>
## 50 1 Yes <NA>
## 51 NA Yes <NA>
## 52 NA Yes <NA>
## 53 0 Yes <NA>
## 54 NA Yes <NA>
## 55 NA Yes <NA>
## 56 NA Yes <NA>
## 57 NA Yes <NA>
## 58 0 Yes <NA>
## 59 0 Yes <NA>
## 60 NA Yes <NA>
## 61 NA Yes <NA>
## 62 0 Yes <NA>
## 63 0 Yes <NA>
## 64 0 Yes <NA>
## 65 1 Yes <NA>
## 66 1 Yes <NA>
## 67 NA Yes <NA>
## 68 NA Yes <NA>
## 69 1 Yes <NA>
## 70 0 Yes <NA>
## 71 NA Yes <NA>
## 72 NA Yes <NA>
## 73 NA Yes <NA>
## 74 1 Yes <NA>
## 75 1 Yes <NA>
## 76 0 Yes <NA>
## 77 1 Yes <NA>
## 78 0 Yes <NA>
## 79 0 Yes <NA>
## 80 NA Yes <NA>
## 81 1 Yes Yes
## 82 NA Yes Yes
## 83 NA Yes Yes
## 84 NA Yes Yes
## 85 NA Yes Yes
## 86 0 Yes Yes
## 87 NA Yes Yes
## 88 NA Yes Yes
## 89 NA Yes Yes
## 90 NA Yes Yes
## 91 1 Yes <NA>
## 92 NA Yes <NA>
## 93 0 Yes <NA>
## 94 NA Yes <NA>
## 95 0 Yes <NA>
## 96 0 Yes <NA>
## 97 NA Yes <NA>
## 98 NA Yes <NA>
## 99 NA Yes <NA>
## 100 NA Yes <NA>
## 101 NA Yes <NA>
## 102 NA Yes <NA>
## 103 NA Yes <NA>
## 104 NA Yes <NA>
## 105 NA Yes <NA>
## 106 NA Yes <NA>
## 107 NA Yes <NA>
## 108 NA Yes <NA>
## 109 NA Yes <NA>
## 110 NA Yes <NA>
## 111 NA Yes <NA>
## 112 NA Yes <NA>
## 113 NA Yes <NA>
## 114 NA Yes <NA>
## 115 NA Yes <NA>
## 116 NA Yes <NA>
## 117 NA Yes <NA>
## 118 0 Yes <NA>
## 119 0 Yes <NA>
## 120 1 Yes <NA>
## 121 0 Yes <NA>
## 122 NA Yes <NA>
## 123 1 Yes <NA>
## 124 0 Yes <NA>
## 125 0 Yes <NA>
## 126 NA Yes <NA>
## 127 NA Yes <NA>
## 128 NA Yes <NA>
## 129 NA Yes <NA>
## 130 NA Yes <NA>
## 131 NA Yes <NA>
## 132 NA Yes <NA>
## 133 NA Yes <NA>
## 134 NA Yes <NA>
## 135 NA Yes <NA>
## 136 NA Yes <NA>
## 137 1 Yes <NA>
## 138 NA Yes Yes
## 139 NA Yes Yes
## 140 1 Yes Yes
## 141 0 Yes Yes
## 142 NA Yes Yes
## 143 NA Yes Yes
## 144 NA Yes Yes
## 145 NA Yes Yes
## 146 NA Yes Yes
## 147 NA Yes Yes
## 148 0 Yes <NA>
## 149 NA Yes <NA>
## 150 NA Yes <NA>
## 151 1 Yes <NA>
## 152 1 Yes <NA>
## 153 0 Yes <NA>
## 154 1 Yes <NA>
## 155 NA Yes <NA>
## 156 0 Yes <NA>
## 157 0 Yes <NA>
## 158 0 Yes <NA>
## 159 0 Yes <NA>
## 160 0 Yes <NA>
## 161 0 Yes <NA>
## 162 1 Yes <NA>
## 163 0 Yes <NA>
## 164 NA Yes <NA>
## 165 NA Yes <NA>
## 166 NA Yes <NA>
## 167 NA Yes <NA>
## 168 NA Yes <NA>
## 169 NA Yes <NA>
## 170 NA Yes <NA>
## 171 NA Yes <NA>
## 172 NA Yes <NA>
## 173 NA Yes <NA>
## 174 NA Yes <NA>
## 175 0 Yes <NA>
## 176 0 Yes <NA>
## 177 NA Yes <NA>
## 178 0 Yes <NA>
## 179 1 Yes <NA>
## 180 0 Yes <NA>
## 181 0 Yes <NA>
## 182 1 Yes <NA>
## 183 0 Yes <NA>
## 184 0 Yes <NA>
## 185 NA Yes <NA>
## 186 NA Yes <NA>
## 187 0 Yes <NA>
## 188 0 Yes <NA>
## 189 NA Yes <NA>
## 190 NA Yes <NA>
## 191 0 Yes <NA>
## 192 NA Yes <NA>
## 193 NA Yes <NA>
## 194 0 Yes <NA>
## 195 NA Yes <NA>
## 196 NA Yes <NA>
## 197 NA Yes <NA>
## 198 NA Yes <NA>
## 199 0 Yes <NA>
## 200 0 Yes <NA>
## 201 0 Yes <NA>
## 202 NA Yes <NA>
## 203 0 Yes <NA>
## 204 NA Yes <NA>
## 205 NA Yes <NA>
## 206 NA Yes <NA>
## 207 NA Yes Yes
## 208 NA Yes Yes
## 209 NA Yes Yes
## 210 NA Yes Yes
## 211 1 Yes Yes
## 212 NA Yes <NA>
## 213 0 Yes Yes
## 214 NA Yes Yes
## 215 NA Yes Yes
## 216 NA Yes Yes
## 217 NA Yes Yes
## 218 0 Yes <NA>
## 219 NA Yes <NA>
## 220 0 Yes <NA>
## 221 NA Yes <NA>
## 222 NA Yes <NA>
## 223 NA Yes <NA>
## 224 NA Yes <NA>
## 225 0 Yes <NA>
## 226 NA Yes <NA>
## 227 NA Yes <NA>
## used_pilot_analysis wg_norm_depth ecotype sex samp_length O(HOM)
## 1 <NA> 0.6849506 Marine female 15 3382
## 2 <NA> 2.3553292 Freshwater female 14 3852
## 3 <NA> 1.4800345 Freshwater female 14 3619
## 4 <NA> 0.5374682 Marine female 4 730
## 5 <NA> 0.7026082 Marine female 14 3304
## 6 <NA> 0.5020058 Marine female 4 1278
## 7 <NA> 1.6359764 Freshwater female 4 2192
## 8 <NA> 0.8270414 Freshwater female 14 3373
## 9 <NA> 1.0066092 Freshwater female 4 2153
## 10 <NA> 1.0850730 Marine female 4 1654
## 11 <NA> 0.4878640 Marine female 17 2830
## 12 <NA> 0.8651173 Freshwater female 18 2984
## 13 <NA> 1.3731481 Marine female 17 3307
## 14 <NA> 0.8045064 Marine male 17 2741
## 15 <NA> 1.3648798 Marine female 17 2831
## 16 <NA> 2.1426930 Marine female 17 3382
## 17 <NA> 1.4323414 Freshwater female 18 3438
## 18 <NA> 1.4249474 Freshwater female 18 3466
## 19 <NA> 1.7432035 Freshwater female 18 3300
## 20 <NA> 1.4343480 Freshwater female 18 3414
## 21 <NA> 1.0560614 Marine female 11 3416
## 22 <NA> 0.2388108 Freshwater male 14 2189
## 23 <NA> 1.2917555 Freshwater female 14 3688
## 24 <NA> 3.3653854 Freshwater female 14 3822
## 25 <NA> 0.7454121 Marine female 14 3304
## 26 <NA> 1.1370784 Marine female 14 2929
## 27 <NA> 1.0638894 Marine female 14 2845
## 28 <NA> 0.6918268 Marine female 14 3139
## 29 <NA> 0.7529911 Marine female 14 3252
## 30 <NA> 1.5920580 Freshwater female 13 3806
## 31 <NA> 1.5142402 Freshwater female 14 3788
## 32 <NA> 1.5162031 Freshwater female 14 3816
## 33 <NA> 1.5549021 Freshwater female 14 3711
## 34 <NA> 1.1345803 Freshwater male 14 2684
## 35 <NA> 2.3256956 Freshwater female 14 3743
## 36 <NA> 1.4180224 Freshwater female 14 3868
## 37 <NA> 1.4086799 Freshwater female 14 3827
## 38 <NA> 2.3403841 Freshwater female 14 3864
## 39 <NA> 1.3365278 Freshwater female 14 3847
## 40 <NA> 0.6451813 Marine female 15 3188
## 41 <NA> 1.5962013 Freshwater female 11 3377
## 42 <NA> 1.6120307 Freshwater female 14 3710
## 43 <NA> 2.2106712 Freshwater female 14 3694
## 44 <NA> 0.8067502 Freshwater female 17 3820
## 45 <NA> 1.7448533 Freshwater female 14 3150
## 46 <NA> 2.1585358 Freshwater female 14 3830
## 47 <NA> 1.6178348 Freshwater female 14 3749
## 48 <NA> 2.3118221 Freshwater female 11 3817
## 49 <NA> 0.7622220 Freshwater female 14 3321
## 50 <NA> 0.7169636 Marine female 14 3274
## 51 <NA> 2.2276141 Freshwater female 14 3811
## 52 <NA> 1.4107704 Freshwater female 14 3821
## 53 <NA> 1.6770689 Freshwater female 14 3865
## 54 <NA> 2.4247752 Freshwater female 14 3836
## 55 <NA> 1.7239491 Freshwater female 14 3804
## 56 <NA> 2.3930454 Freshwater female 14 3871
## 57 <NA> 0.5948837 Freshwater female 14 3836
## 58 <NA> 1.4550171 Freshwater female 13 3548
## 59 <NA> 1.6439918 Freshwater female 16 3800
## 60 <NA> 0.6504109 Freshwater female 15 3238
## 61 <NA> 1.3984490 Freshwater female 15 3477
## 62 <NA> 1.4375823 Freshwater female 11 3537
## 63 <NA> 2.2386367 Freshwater female 3 3093
## 64 <NA> 1.5359807 Freshwater female 12 3439
## 65 <NA> 0.7497102 Marine female 14 3455
## 66 <NA> 0.5168126 Marine female 4 1545
## 67 <NA> 1.7884366 Freshwater female 14 3816
## 68 <NA> 1.4725381 Freshwater female 11 3798
## 69 <NA> 0.5199243 Marine female 4 1396
## 70 <NA> 2.9629871 Freshwater female 11 3763
## 71 <NA> 0.7757477 Freshwater female 14 3321
## 72 <NA> 1.3699633 Freshwater female 4 3075
## 73 <NA> 2.2706840 Freshwater female 14 3851
## 74 <NA> 0.4848061 Marine female 4 1614
## 75 <NA> 0.7417463 Marine female 14 3268
## 76 <NA> 2.4103507 Freshwater female 14 3880
## 77 <NA> 0.7306434 Marine female 14 3329
## 78 <NA> 0.7965982 Freshwater female 14 3374
## 79 <NA> 0.8038521 Freshwater female 14 3419
## 80 <NA> 1.6028817 Freshwater male 15 3103
## 81 <NA> 0.5343506 Marine female 18 2843
## 82 <NA> 0.4699554 Marine female 18 2989
## 83 <NA> 0.3299840 Marine female 17 2314
## 84 <NA> 0.4799899 Marine female 17 2748
## 85 <NA> 0.6461944 Marine female 18 3164
## 86 <NA> 1.2179789 Freshwater female 19 3667
## 87 <NA> 0.9831386 Freshwater female 19 3670
## 88 <NA> 1.0767117 Freshwater female 19 3454
## 89 <NA> 1.3577359 Freshwater female 19 3791
## 90 <NA> 1.0543748 Freshwater female 19 3421
## 91 <NA> 0.8396474 Marine female 14 3429
## 92 <NA> 2.4631694 Freshwater female 11 3820
## 93 <NA> 1.4118588 Freshwater female 11 3765
## 94 <NA> 0.7804802 Freshwater female 14 3503
## 95 <NA> 3.0811556 Freshwater female 14 3735
## 96 <NA> 1.0810420 Freshwater female 14 3669
## 97 Yes 1.6117281 Freshwater female 14 2833
## 98 Yes 1.5025336 Freshwater female 14 2987
## 99 Yes 2.4864625 Freshwater female 14 3909
## 100 Yes 1.5502741 Freshwater female 14 2938
## 101 Yes 0.7193451 Freshwater female 14 3453
## 102 Yes 1.5097536 Freshwater female 14 2950
## 103 Yes 2.3767589 Freshwater female 14 3865
## 104 Yes 2.2882877 Freshwater female 14 3835
## 105 Yes 1.5083706 Freshwater female 14 2960
## 106 Yes 0.7557168 Freshwater female 14 3352
## 107 Yes 1.4453855 Freshwater female 14 2953
## 108 Yes 1.5498538 Freshwater female 14 2940
## 109 Yes 1.6572115 Freshwater female 14 3032
## 110 Yes 1.5847858 Freshwater female 14 2952
## 111 Yes 2.2934529 Freshwater female 14 3826
## 112 Yes 1.4353749 Freshwater female 14 3060
## 113 Yes 1.5178244 Freshwater female 14 2920
## 114 Yes 0.7958166 Freshwater female 14 3463
## 115 Yes 2.4039353 Freshwater female 14 3855
## 116 Yes 2.4703915 Freshwater female 14 3810
## 117 <NA> 2.1904474 Freshwater female 14 3878
## 118 <NA> 1.9278299 Freshwater female 14 3834
## 119 <NA> 1.1286510 Freshwater female 14 3336
## 120 <NA> 0.7669173 Marine female 14 3384
## 121 <NA> 0.8153668 Freshwater female 14 3375
## 122 <NA> 1.3803423 Freshwater female 16 3835
## 123 <NA> 0.7993700 Marine female 11 3438
## 124 <NA> 0.5472449 Freshwater female 4 2083
## 125 <NA> 1.3804163 Freshwater female 14 3829
## 126 <NA> 2.4248297 Freshwater female 14 3724
## 127 <NA> 2.4315721 Freshwater female 14 3846
## 128 <NA> 2.0464793 Freshwater female 14 3867
## 129 <NA> 2.3466848 Freshwater female 14 3845
## 130 <NA> 2.3908255 Freshwater female 14 3807
## 131 <NA> 2.5373425 Freshwater female 14 3877
## 132 <NA> 2.8931416 Freshwater female 14 3822
## 133 <NA> 2.4703497 Freshwater female 14 3828
## 134 <NA> 1.6373087 Freshwater female 14 3858
## 135 <NA> 2.0861268 Freshwater female 14 3894
## 136 <NA> 2.0477834 Freshwater female 14 3867
## 137 <NA> 0.5498641 Marine female 14 3268
## 138 <NA> 1.1991732 Marine female 17 3397
## 139 <NA> 1.1202526 Marine female 17 3272
## 140 <NA> 1.0399630 Marine female 17 3371
## 141 <NA> 2.1189994 Freshwater female 17 3761
## 142 <NA> 2.8106664 Freshwater female 17 3754
## 143 <NA> 1.9798941 Freshwater male 17 3017
## 144 <NA> 2.2538158 Freshwater male 17 3005
## 145 <NA> 2.4949858 Freshwater female 17 3818
## 146 <NA> 1.1200892 Marine female 17 3422
## 147 <NA> 0.7969474 Marine female 17 3311
## 148 <NA> 1.5546823 Freshwater female 11 3838
## 149 <NA> 2.2903742 Freshwater female 14 3839
## 150 <NA> 1.9395086 Freshwater female 4 2478
## 151 <NA> 0.5037678 Marine female 3 197
## 152 <NA> 0.7710100 Marine female 14 3419
## 153 <NA> 2.2538990 Freshwater female 4 912
## 154 <NA> 0.8424049 Marine male 11 2143
## 155 <NA> 0.6851245 Freshwater female 14 3297
## 156 <NA> 0.3658525 Freshwater female 14 2349
## 157 <NA> 2.0102874 Freshwater female 14 3740
## 158 <NA> 1.8538903 Freshwater female 4 2716
## 159 <NA> 1.5355805 Freshwater female 11 3410
## 160 <NA> 0.7264593 Freshwater female 17 3303
## 161 <NA> 0.8395585 Freshwater female 14 3242
## 162 <NA> 0.5258849 Marine female 4 2078
## 163 <NA> 2.2118217 Freshwater female 14 3732
## 164 <NA> 1.4440474 Freshwater female 14 3830
## 165 <NA> 2.0801201 Freshwater female 17 3874
## 166 <NA> 1.7273823 Freshwater female 17 3682
## 167 <NA> 2.0801138 Freshwater female 17 3765
## 168 <NA> 2.0695983 Freshwater female 17 3778
## 169 <NA> 1.8873688 Freshwater female 17 3783
## 170 <NA> 2.0130179 Freshwater female 17 3793
## 171 <NA> 1.7134147 Freshwater female 17 3739
## 172 <NA> 2.0014454 Freshwater female 17 3471
## 173 <NA> 1.7836642 Freshwater female 17 3817
## 174 <NA> 1.3158736 Freshwater female 17 3843
## 175 <NA> 1.4627901 Freshwater male 14 2867
## 176 <NA> 1.5287475 Freshwater female 14 3704
## 177 <NA> 2.2190249 Freshwater female 15 3875
## 178 <NA> 1.7042274 Freshwater female 16 3465
## 179 <NA> 0.4274776 Marine female 4 1098
## 180 <NA> 1.8284334 Freshwater female 11 3364
## 181 <NA> 1.5050920 Freshwater female 11 3463
## 182 <NA> 1.4493094 Marine female 15 3360
## 183 <NA> 1.4687684 Freshwater female 16 3519
## 184 <NA> 1.6909613 Freshwater female 3 2000
## 185 <NA> 0.6946182 Freshwater female 14 3836
## 186 <NA> 1.9538070 Freshwater female 4 2595
## 187 <NA> 1.4150368 Freshwater female 14 3322
## 188 <NA> 1.3995207 Freshwater female 16 3377
## 189 <NA> 2.0975582 Freshwater female 14 3812
## 190 <NA> 2.2469878 Freshwater female 11 3841
## 191 <NA> 1.4931082 Freshwater female 16 3399
## 192 <NA> 2.4351882 Freshwater female 14 3901
## 193 <NA> 1.5232511 Freshwater female 17 3740
## 194 <NA> 1.3564929 Freshwater female 14 2844
## 195 <NA> 0.6574794 Freshwater female 14 3405
## 196 <NA> 1.6187013 Freshwater female 14 3802
## 197 <NA> 2.0666936 Freshwater female 14 3818
## 198 <NA> 2.3104468 Freshwater female 14 3827
## 199 <NA> 1.9013754 Freshwater female 15 3764
## 200 <NA> 1.4531613 Freshwater female 14 3335
## 201 <NA> 2.1253919 Freshwater female 14 3878
## 202 <NA> 2.3799560 Freshwater female 14 3843
## 203 <NA> 1.5878779 Freshwater female 11 3390
## 204 <NA> 2.2704204 Freshwater female 14 3857
## 205 <NA> 0.7655431 Freshwater female 14 3489
## 206 <NA> 1.0359457 Marine female 6 1617
## 207 <NA> 0.6902854 Marine female 14 3302
## 208 <NA> 0.6985398 Marine female 14 3323
## 209 <NA> 0.7077671 Marine female 14 3246
## 210 <NA> 1.3967808 Marine female 14 2939
## 211 <NA> 0.6833612 Marine female 14 3300
## 212 <NA> 1.2769341 Freshwater female 6 2845
## 213 <NA> 1.4403373 Freshwater female 15 3676
## 214 <NA> 1.4511802 Freshwater female 15 3761
## 215 <NA> 1.4965529 Freshwater female 15 3688
## 216 <NA> 1.4960439 Freshwater female 15 3619
## 217 <NA> 1.4551439 Freshwater female 15 3677
## 218 <NA> 2.2754477 Freshwater female 11 3812
## 219 <NA> 2.3915016 Freshwater female 11 3752
## 220 <NA> 2.3704515 Freshwater female 11 3805
## 221 <NA> 1.9100514 Freshwater female 14 3827
## 222 <NA> 1.7311785 Freshwater female 14 3800
## 223 <NA> 1.3781070 Freshwater female 14 3403
## 224 <NA> 1.8285512 Freshwater female 14 3591
## 225 <NA> 0.4127094 Freshwater female 16 2122
## 226 <NA> 2.1754838 Freshwater female 14 3778
## 227 <NA> 2.9936058 Freshwater female 14 3811
## E(HOM) N_SITES F prop_hom prop_het het_status
## 1 2871.6 3510 0.79950 0.9635328 0.036467236 likely-hom
## 2 3150.2 3856 0.99433 0.9989627 0.001037344 likely-hom
## 3 3043.1 3725 0.84454 0.9715436 0.028456376 likely-hom
## 4 658.9 732 0.97265 0.9972678 0.002732240 likely-hom
## 5 2800.8 3421 0.81134 0.9657995 0.034200526 likely-hom
## 6 1142.2 1283 0.96450 0.9961029 0.003897116 likely-hom
## 7 1779.7 2192 1.00000 1.0000000 0.000000000 likely-hom
## 8 2811.3 3439 0.89485 0.9808084 0.019191625 likely-hom
## 9 1828.9 2188 0.90253 0.9840037 0.015996344 likely-hom
## 10 1402.8 1670 0.94011 0.9904192 0.009580838 likely-hom
## 11 2393.5 2911 0.84347 0.9721745 0.027825490 likely-hom
## 12 2493.0 3040 0.89762 0.9815789 0.018421053 likely-hom
## 13 2870.1 3509 0.68381 0.9424337 0.057566258 likely-hom
## 14 2796.2 3400 -0.09135 0.8061765 0.193823529 likely-het
## 15 2892.7 3524 -0.09765 0.8033485 0.196651532 likely-het
## 16 2882.7 3518 0.78594 0.9613417 0.038658329 likely-hom
## 17 2857.3 3497 0.90777 0.9831284 0.016871604 likely-hom
## 18 2919.5 3575 0.83372 0.9695105 0.030489510 likely-hom
## 19 2906.7 3561 0.60110 0.9267060 0.073294019 likely-hom
## 20 2832.0 3466 0.91798 0.9849971 0.015002885 likely-hom
## 21 2916.3 3576 0.75748 0.9552573 0.044742729 likely-hom
## 22 1945.2 2368 0.57658 0.9244088 0.075591216 likely-hom
## 23 3030.8 3705 0.97478 0.9954116 0.004588394 likely-hom
## 24 3132.9 3835 0.98148 0.9966102 0.003389831 likely-hom
## 25 2803.8 3425 0.80521 0.9646715 0.035328467 likely-hom
## 26 3092.9 3770 -0.24210 0.7769231 0.223076923 likely-het
## 27 2807.9 3419 0.06066 0.8321147 0.167885347 likely-het
## 28 2675.6 3255 0.79978 0.9643625 0.035637481 likely-hom
## 29 2737.1 3341 0.85263 0.9733613 0.026638731 likely-hom
## 30 3129.6 3831 0.96435 0.9934743 0.006525711 likely-hom
## 31 3130.2 3830 0.93998 0.9890339 0.010966057 likely-hom
## 32 3145.8 3846 0.95715 0.9921997 0.007800312 likely-hom
## 33 3065.9 3747 0.94715 0.9903923 0.009607686 likely-hom
## 34 2690.2 3275 -0.01055 0.8195420 0.180458015 likely-het
## 35 3119.2 3819 0.89140 0.9800995 0.019900498 likely-hom
## 36 3169.7 3878 0.98588 0.9974214 0.002578649 likely-hom
## 37 3140.0 3841 0.98003 0.9963551 0.003644884 likely-hom
## 38 3162.7 3873 0.98733 0.9976762 0.002323780 likely-hom
## 39 3153.6 3862 0.97882 0.9961160 0.003883998 likely-hom
## 40 2720.2 3322 0.77734 0.9596629 0.040337146 likely-hom
## 41 2953.4 3609 0.64613 0.9357163 0.064283735 likely-hom
## 42 3038.3 3714 0.99408 0.9989230 0.001077006 likely-hom
## 43 3080.2 3767 0.89371 0.9806212 0.019378816 likely-hom
## 44 3139.3 3842 0.96869 0.9942738 0.005726184 likely-hom
## 45 3020.5 3694 0.19233 0.8527342 0.147265836 likely-het
## 46 3160.4 3865 0.95033 0.9909444 0.009055627 likely-hom
## 47 3082.3 3767 0.97371 0.9952217 0.004778338 likely-hom
## 48 3127.5 3828 0.98430 0.9971264 0.002873563 likely-hom
## 49 2833.8 3463 0.77432 0.9589951 0.041004909 likely-hom
## 50 2823.6 3450 0.71903 0.9489855 0.051014493 likely-hom
## 51 3138.4 3841 0.95730 0.9921895 0.007810466 likely-hom
## 52 3135.5 3841 0.97165 0.9947930 0.005206977 likely-hom
## 53 3183.1 3895 0.95786 0.9922978 0.007702182 likely-hom
## 54 3177.7 3890 0.92419 0.9861183 0.013881748 likely-hom
## 55 3185.8 3898 0.86801 0.9758851 0.024114931 likely-hom
## 56 3178.1 3891 0.97195 0.9948599 0.005140067 likely-hom
## 57 3155.0 3862 0.96322 0.9932677 0.006732263 likely-hom
## 58 2954.2 3611 0.90408 0.9825533 0.017446691 likely-hom
## 59 3127.3 3825 0.96417 0.9934641 0.006535948 likely-hom
## 60 2742.8 3354 0.81022 0.9654144 0.034585569 likely-hom
## 61 2892.1 3537 0.90696 0.9830365 0.016963528 likely-hom
## 62 2950.9 3608 0.89194 0.9803215 0.019678492 likely-hom
## 63 2529.3 3103 0.98257 0.9967773 0.003222688 likely-hom
## 64 2921.1 3570 0.79812 0.9633053 0.036694678 likely-hom
## 65 2851.3 3487 0.94966 0.9908231 0.009176943 likely-hom
## 66 1373.2 1555 0.94499 0.9935691 0.006430868 likely-hom
## 67 3188.2 3903 0.87829 0.9777095 0.022290546 likely-hom
## 68 3144.1 3846 0.93161 0.9875195 0.012480499 likely-hom
## 69 1244.6 1400 0.97425 0.9971429 0.002857143 likely-hom
## 70 3116.4 3813 0.92822 0.9868870 0.013113034 likely-hom
## 71 2782.3 3394 0.88067 0.9784915 0.021508544 likely-hom
## 72 2524.7 3083 0.98567 0.9974051 0.002594875 likely-hom
## 73 3159.6 3869 0.97463 0.9953476 0.004652365 likely-hom
## 74 1426.7 1619 0.97400 0.9969117 0.003088326 likely-hom
## 75 2800.4 3418 0.75712 0.9561147 0.043885313 likely-hom
## 76 3173.0 3885 0.99298 0.9987130 0.001287001 likely-hom
## 77 2841.9 3472 0.77306 0.9588134 0.041186636 likely-hom
## 78 2804.1 3428 0.91344 0.9842474 0.015752625 likely-hom
## 79 2850.6 3484 0.89738 0.9813433 0.018656716 likely-hom
## 80 3116.4 3806 -0.01947 0.8152916 0.184708355 likely-het
## 81 2490.5 3007 0.68246 0.9454606 0.054539408 likely-hom
## 82 2542.0 3092 0.81274 0.9666882 0.033311772 likely-hom
## 83 1989.0 2373 0.84635 0.9751370 0.024863043 likely-hom
## 84 2344.4 2853 0.79355 0.9631966 0.036803365 likely-hom
## 85 2683.7 3275 0.81229 0.9661069 0.033893130 likely-hom
## 86 3010.0 3687 0.97046 0.9945755 0.005424464 likely-hom
## 87 3031.0 3709 0.94248 0.9894850 0.010514964 likely-hom
## 88 2832.7 3471 0.97336 0.9951023 0.004897724 likely-hom
## 89 3118.2 3821 0.95731 0.9921487 0.007851348 likely-hom
## 90 2821.9 3444 0.96303 0.9933217 0.006678281 likely-hom
## 91 2887.2 3534 0.83765 0.9702886 0.029711375 likely-hom
## 92 3155.3 3862 0.94057 0.9891248 0.010875194 likely-hom
## 93 3087.5 3779 0.97975 0.9962953 0.003704684 likely-hom
## 94 2934.7 3600 0.85420 0.9730556 0.026944444 likely-hom
## 95 3072.3 3758 0.96646 0.9938797 0.006120277 likely-hom
## 96 3036.1 3715 0.93224 0.9876178 0.012382234 likely-hom
## 97 3048.8 3714 -0.32440 0.7627894 0.237210555 likely-het
## 98 3091.4 3768 -0.15428 0.7927282 0.207271762 likely-het
## 99 3208.1 3927 0.97496 0.9954163 0.004583652 likely-hom
## 100 3128.0 3819 -0.27490 0.7693113 0.230688662 likely-het
## 101 2892.5 3538 0.86833 0.9759751 0.024024873 likely-hom
## 102 3107.2 3793 -0.22921 0.7777485 0.222251516 likely-het
## 103 3208.3 3926 0.91501 0.9844626 0.015537443 likely-hom
## 104 3181.7 3893 0.91846 0.9851015 0.014898536 likely-hom
## 105 3034.7 3694 -0.11328 0.8012994 0.198700596 likely-het
## 106 2861.8 3498 0.77051 0.9582619 0.041738136 likely-hom
## 107 3049.8 3711 -0.14646 0.7957424 0.204257613 likely-het
## 108 3064.8 3743 -0.18401 0.7854662 0.214533796 likely-het
## 109 3083.6 3760 -0.07622 0.8063830 0.193617021 likely-het
## 110 3064.7 3726 -0.17045 0.7922705 0.207729469 likely-het
## 111 3139.9 3841 0.97861 0.9960948 0.003905233 likely-hom
## 112 3077.4 3750 -0.02594 0.8160000 0.184000000 likely-het
## 113 3103.1 3790 -0.26660 0.7704485 0.229551451 likely-het
## 114 2971.2 3641 0.73425 0.9511123 0.048887668 likely-hom
## 115 3183.4 3896 0.94246 0.9894764 0.010523614 likely-hom
## 116 3158.2 3862 0.92612 0.9865355 0.013464526 likely-hom
## 117 3195.7 3911 0.95387 0.9915623 0.008437740 likely-hom
## 118 3153.4 3860 0.96320 0.9932642 0.006735751 likely-hom
## 119 2957.8 3620 0.57111 0.9215470 0.078453039 likely-hom
## 120 2803.3 3428 0.92956 0.9871645 0.012835473 likely-hom
## 121 2822.0 3455 0.87362 0.9768452 0.023154848 likely-hom
## 122 3148.5 3852 0.97583 0.9955867 0.004413292 likely-hom
## 123 2921.5 3570 0.79644 0.9630252 0.036974790 likely-hom
## 124 1735.0 2086 0.99145 0.9985618 0.001438159 likely-hom
## 125 3175.5 3888 0.91720 0.9848251 0.015174897 likely-hom
## 126 3091.7 3781 0.91731 0.9849246 0.015075377 likely-hom
## 127 3172.1 3884 0.94662 0.9902163 0.009783728 likely-hom
## 128 3179.9 3888 0.97034 0.9945988 0.005401235 likely-hom
## 129 3158.8 3867 0.96893 0.9943108 0.005689165 likely-hom
## 130 3124.7 3827 0.97152 0.9947740 0.005226026 likely-hom
## 131 3192.4 3909 0.95534 0.9918138 0.008186237 likely-hom
## 132 3182.2 3895 0.89758 0.9812580 0.018741977 likely-hom
## 133 3186.5 3897 0.90289 0.9822941 0.017705928 likely-hom
## 134 3170.6 3884 0.96356 0.9933059 0.006694130 likely-hom
## 135 3202.6 3918 0.96645 0.9938744 0.006125574 likely-hom
## 136 3192.9 3908 0.94266 0.9895087 0.010491300 likely-hom
## 137 2783.3 3407 0.77713 0.9592016 0.040798356 likely-hom
## 138 2892.0 3539 0.78052 0.9598757 0.040124329 likely-hom
## 139 2785.9 3399 0.79286 0.9626361 0.037363931 likely-hom
## 140 2863.8 3504 0.79226 0.9620434 0.037956621 likely-hom
## 141 3138.5 3843 0.88361 0.9786625 0.021337497 likely-hom
## 142 3100.3 3796 0.93963 0.9889357 0.011064278 likely-hom
## 143 2954.2 3615 0.09499 0.8345781 0.165421853 likely-het
## 144 2990.2 3652 0.02239 0.8228368 0.177163198 likely-het
## 145 3135.8 3840 0.96876 0.9942708 0.005729167 likely-hom
## 146 2893.7 3543 0.81365 0.9658482 0.034151849 likely-hom
## 147 2826.1 3452 0.77471 0.9591541 0.040845886 likely-hom
## 148 3150.2 3854 0.97727 0.9958485 0.004151531 likely-hom
## 149 3154.7 3856 0.97576 0.9955913 0.004408714 likely-hom
## 150 2029.0 2478 1.00000 1.0000000 0.000000000 likely-hom
## 151 175.3 197 1.00000 1.0000000 0.000000000 likely-hom
## 152 2867.9 3516 0.85032 0.9724118 0.027588168 likely-hom
## 153 751.3 912 1.00000 1.0000000 0.000000000 likely-hom
## 154 2056.6 2481 0.20354 0.8637646 0.136235389 likely-het
## 155 2797.5 3426 0.79475 0.9623468 0.037653240 likely-hom
## 156 1969.8 2372 0.94282 0.9903035 0.009696459 likely-hom
## 157 3088.0 3781 0.94083 0.9891563 0.010843692 likely-hom
## 158 2224.3 2722 0.98795 0.9977957 0.002204262 likely-hom
## 159 2877.2 3519 0.83017 0.9690253 0.030974709 likely-hom
## 160 2762.4 3371 0.88827 0.9798279 0.020172056 likely-hom
## 161 2683.3 3272 0.94904 0.9908313 0.009168704 likely-hom
## 162 1790.6 2092 0.95355 0.9933078 0.006692161 likely-hom
## 163 3066.5 3750 0.97366 0.9952000 0.004800000 likely-hom
## 164 3153.9 3856 0.96297 0.9932573 0.006742739 likely-hom
## 165 3181.1 3895 0.97058 0.9946085 0.005391528 likely-hom
## 166 3035.3 3714 0.95285 0.9913840 0.008616047 likely-hom
## 167 3099.9 3793 0.95960 0.9926180 0.007382020 likely-hom
## 168 3119.5 3815 0.94680 0.9903014 0.009698558 likely-hom
## 169 3129.5 3827 0.93692 0.9885027 0.011497256 likely-hom
## 170 3138.7 3840 0.93298 0.9877604 0.012239583 likely-hom
## 171 3070.6 3759 0.97095 0.9946794 0.005320564 likely-hom
## 172 2850.4 3480 0.98571 0.9974138 0.002586207 likely-hom
## 173 3147.5 3852 0.95032 0.9909138 0.009086189 likely-hom
## 174 3151.9 3860 0.97599 0.9955959 0.004404145 likely-hom
## 175 2767.0 3358 0.16922 0.8537820 0.146217987 likely-het
## 176 3057.6 3741 0.94586 0.9901096 0.009890404 likely-hom
## 177 3180.8 3896 0.97064 0.9946099 0.005390144 likely-hom
## 178 2896.1 3541 0.88215 0.9785371 0.021462864 likely-hom
## 179 987.6 1100 0.98221 0.9981818 0.001818182 likely-hom
## 180 2992.8 3656 0.55968 0.9201313 0.079868709 likely-hom
## 181 2879.6 3519 0.91242 0.9840864 0.015913612 likely-hom
## 182 2905.6 3556 0.69863 0.9448819 0.055118110 likely-hom
## 183 2928.5 3589 0.89401 0.9804960 0.019504040 likely-hom
## 184 1629.9 2000 1.00000 1.0000000 0.000000000 likely-hom
## 185 3160.1 3867 0.95615 0.9919834 0.008016550 likely-hom
## 186 2117.8 2596 0.99791 0.9996148 0.000385208 likely-hom
## 187 2736.5 3344 0.96379 0.9934211 0.006578947 likely-hom
## 188 2916.7 3570 0.70457 0.9459384 0.054061625 likely-hom
## 189 3140.6 3844 0.95451 0.9916753 0.008324662 likely-hom
## 190 3195.0 3912 0.90097 0.9818507 0.018149284 likely-hom
## 191 2849.0 3479 0.87301 0.9770049 0.022995114 likely-hom
## 192 3197.7 3913 0.98322 0.9969333 0.003066701 likely-hom
## 193 3135.6 3838 0.86048 0.9744659 0.025534132 likely-hom
## 194 3102.1 3788 -0.37631 0.7507920 0.249208025 likely-het
## 195 2837.3 3475 0.89024 0.9798561 0.020143885 likely-hom
## 196 3136.2 3839 0.94736 0.9903621 0.009637927 likely-hom
## 197 3154.4 3860 0.94048 0.9891192 0.010880829 likely-hom
## 198 3156.6 3864 0.94770 0.9904244 0.009575569 likely-hom
## 199 3136.6 3834 0.89962 0.9817423 0.018257694 likely-hom
## 200 2885.8 3531 0.69622 0.9444916 0.055508355 likely-hom
## 201 3176.7 3888 0.98594 0.9974280 0.002572016 likely-hom
## 202 3170.6 3876 0.95322 0.9914861 0.008513932 likely-hom
## 203 2885.6 3524 0.79010 0.9619750 0.038024972 likely-hom
## 204 3163.2 3869 0.98300 0.9968984 0.003101577 likely-hom
## 205 2917.6 3579 0.86392 0.9748533 0.025146689 likely-hom
## 206 1374.9 1624 0.97190 0.9956897 0.004310345 likely-hom
## 207 2814.1 3446 0.77212 0.9582124 0.041787580 likely-hom
## 208 2804.3 3435 0.82243 0.9673945 0.032605531 likely-hom
## 209 2777.1 3401 0.75158 0.9544252 0.045574831 likely-hom
## 210 2863.1 3473 0.12443 0.8462424 0.153757558 likely-het
## 211 2819.7 3451 0.76080 0.9562446 0.043755433 likely-hom
## 212 2334.2 2848 0.99416 0.9989466 0.001053371 likely-hom
## 213 3039.1 3719 0.93676 0.9884378 0.011562248 likely-hom
## 214 3085.8 3775 0.97969 0.9962914 0.003708609 likely-hom
## 215 3100.7 3799 0.84105 0.9707818 0.029218215 likely-hom
## 216 3050.8 3732 0.83412 0.9697213 0.030278671 likely-hom
## 217 3031.4 3708 0.95418 0.9916397 0.008360302 likely-hom
## 218 3128.4 3827 0.97853 0.9960805 0.003919519 likely-hom
## 219 3077.0 3766 0.97968 0.9962825 0.003717472 likely-hom
## 220 3129.1 3829 0.96571 0.9937320 0.006267955 likely-hom
## 221 3159.5 3865 0.94614 0.9901682 0.009831824 likely-hom
## 222 3137.9 3837 0.94707 0.9903570 0.009642950 likely-hom
## 223 2788.5 3417 0.97772 0.9959028 0.004097161 likely-hom
## 224 2961.1 3619 0.95744 0.9922631 0.007736944 likely-hom
## 225 1764.2 2131 0.97547 0.9957766 0.004223369 likely-hom
## 226 3100.4 3794 0.97693 0.9957828 0.004217185 likely-hom
## 227 3154.7 3859 0.93185 0.9875615 0.012438456 likely-homread_tsv(het_file)## Rows: 227 Columns: 5
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: "\t"
## chr (1): INDV
## dbl (4): O(HOM), E(HOM), N_SITES, F
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.## # A tibble: 227 × 5
## INDV `O(HOM)` `E(HOM)` N_SITES F
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 AKMA|X|2001#102 3382 2872. 3510 0.800
## 2 AKST|X|2001#03 3852 3150. 3856 0.994
## 3 ANSR_X_2009#01 3619 3043. 3725 0.845
## 4 ANTL 730 659. 732 0.973
## 5 BARW|X|2012#04 3304 2801. 3421 0.811
## 6 BDGB 1278 1142. 1283 0.964
## 7 BEPA 2192 1780. 2192 1
## 8 BHAR|X|2011#02 3373 2811. 3439 0.895
## 9 BIGL 2153 1829. 2188 0.903
## 10 BIGR 1654 1403. 1670 0.940
## # ℹ 217 more rows# Get subset of sample names that excludes Jones et al. 2012 samples and males
vcf_samples_filt <- filter(tab_annot, sex=="female", samp_length > 6, desc=="MYH3C3", type=="real")$sample.id# make filtered version of PCA with males and Jones et al. 2012 samples
# Open GDS file
genofile <- snpgdsOpen("all.gds")
# Run PCA
pca_filter <- snpgdsPCA(genofile, num.thread=2, autosome.only = FALSE, sample.id = vcf_samples_filt)## Principal Component Analysis (PCA) on genotypes:
## Excluding 394 SNPs (monomorphic: TRUE, MAF: NaN, missing rate: NaN)
## # of samples: 198
## # of SNPs: 3,176
## using 2 threads
## # of principal components: 32
## PCA: the sum of all selected genotypes (0,1,2) = 985378
## CPU capabilities: Double-Precision SSE2
## Sun Jul 13 22:16:22 2025 (internal increment: 4920)
## [..................................................] 0%, ETC: --- [==================================================] 100%, completed, 1s
## Sun Jul 13 22:16:23 2025 Begin (eigenvalues and eigenvectors)
## Sun Jul 13 22:16:23 2025 Done.snpgdsClose(genofile)
# variance proportion (%)
pc.percent_filter <- round(pca_filter$varprop*100, 2)
PC1_lab_filter <- paste("PC1 (", pc.percent_filter[1], "%)", sep = "")
PC2_lab_filter <- paste("PC2 (", pc.percent_filter[2], "%)", sep = "")
# make a new data.frame
tab_filter <- data.frame(sample.id = pca_filter$sample.id,
EV1_filter = pca_filter$eigenvect[,1], # the first eigenvector
EV2_filter = pca_filter$eigenvect[,2], # the second eigenvector
EV3_filter = pca_filter$eigenvect[,3], # the third eigenvector
EV4_filter = pca_filter$eigenvect[,4], # the fourth eigenvector
stringsAsFactors = FALSE)
# Add to table
tab_annot <- left_join(tab_annot, tab_filter)## Joining with `by = join_by(sample.id)`# Make plot of all PCA without filtering where shape is het status
xlab## function (label)
## {
## labs(x = label)
## }
## <bytecode: 0x2ac85a48a2a0>
## <environment: namespace:ggplot2>tab_annot %>%
ggplot(aes(EV1, EV2, color = wg_norm_depth)) +
geom_point(aes(shape = het_status), size = 3,) +
scale_color_viridis_c(option = "magma") +
theme_cowplot(8) +
panel_border(color="black", size=0.75) +
xlab(PC1_lab) +
ylab(PC2_lab)
# Make plot of all PCA without filtering where shape is het status with labels
tab_annot %>%
ggplot(aes(EV1, EV2, color = wg_norm_depth)) +
geom_point(aes(shape = het_status), size = 3,) +
geom_label_repel(aes(label = acronym),
size = 3,
box.padding = 0.5,
point.padding = 0.5,
max.overlaps = 50,
nudge_x = 0.001,
segment.color = 'grey50') +
scale_color_viridis_c(option = "magma") +
theme_cowplot(20) +
panel_border(color="black", size=0.75) +
xlab(PC1_lab) +
ylab(PC2_lab)## Warning: ggrepel: 108 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
# Make plot of all PCA without filtering where shape is marine or freshwater
tab_annot %>%
ggplot(aes(EV1, EV2, color = wg_norm_depth)) +
geom_point(aes(shape = ecotype), size = 3,) +
scale_color_viridis_c(option = "magma") +
theme_cowplot(8) +
panel_border(color="black", size=0.75) +
xlab(PC1_lab) +
ylab(PC2_lab)
# Make plot of all PCA without filtering where shape is marine or freshwater with labels
tab_annot %>%
ggplot(aes(EV1, EV2, color = wg_norm_depth)) +
geom_point(aes(shape = ecotype), size = 3,) +
scale_color_viridis_c(option = "magma") +
theme_cowplot(20) +
panel_border(color="black", size=0.75) +
geom_label_repel(aes(label = acronym),
size = 3,
box.padding = 0.5,
point.padding = 0.5,
max.overlaps = 50,
nudge_x = 0.001,
segment.color = 'grey50') +
xlab(PC1_lab) +
ylab(PC2_lab)## Warning: ggrepel: 108 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
Remake plots with filtering out males and Jones et al. 2012 samples before running the PCA
# Make plot of filtered PCA where shape is het status
tab_annot %>%
ggplot(aes(EV1_filter, EV2_filter, color = wg_norm_depth)) +
geom_point(aes(shape = het_status), size = 3,) +
scale_color_viridis_c(option = "magma") +
theme_cowplot(8) +
panel_border(color="black", size=0.75) +
xlab(PC1_lab_filter) +
ylab(PC2_lab_filter)## Warning: Removed 29 rows containing missing values or values outside the scale range
## (`geom_point()`).
# Make plot of filtered PCA where shape is het status with labels
tab_annot %>%
ggplot(aes(EV1_filter, EV2_filter, color = wg_norm_depth)) +
geom_point(aes(shape = het_status), size = 3,) +
geom_label_repel(aes(label = acronym),
size = 3,
box.padding = 0.5,
point.padding = 0.5,
max.overlaps = 50,
nudge_x = 0.001,
segment.color = 'grey50') +
scale_color_viridis_c(option = "magma") +
theme_cowplot(20) +
panel_border(color="black", size=0.75) +
xlab(PC1_lab_filter) +
ylab(PC2_lab_filter)## Warning: Removed 29 rows containing missing values or values outside the scale range
## (`geom_point()`).## Warning: Removed 29 rows containing missing values or values outside the scale range
## (`geom_label_repel()`).## Warning: ggrepel: 108 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
# Make plot of filtered PCA where shape is marine or freshwater
tab_annot %>%
ggplot(aes(EV1_filter, EV2_filter, color = wg_norm_depth)) +
geom_point(aes(shape = ecotype), size = 3,) +
scale_color_viridis_c(option = "magma") +
theme_cowplot(8) +
panel_border(color="black", size=0.75) +
xlab(PC1_lab_filter) +
ylab(PC2_lab_filter)## Warning: Removed 29 rows containing missing values or values outside the scale range
## (`geom_point()`).
# Make plot of filtered PCA where shape is marine or freshwater with labels
tab_annot %>%
ggplot(aes(EV1_filter, EV2_filter, color = wg_norm_depth)) +
geom_point(aes(shape = ecotype), size = 3,) +
scale_color_viridis_c(option = "magma") +
theme_cowplot(20) +
panel_border(color="black", size=0.75) +
geom_label_repel(aes(label = acronym),
size = 3,
box.padding = 0.5,
point.padding = 0.5,
max.overlaps = 50,
nudge_x = 0.001,
segment.color = 'grey50') +
xlab(PC1_lab_filter) +
ylab(PC2_lab_filter)## Warning: Removed 29 rows containing missing values or values outside the scale range
## (`geom_point()`).## Warning: Removed 29 rows containing missing values or values outside the scale range
## (`geom_label_repel()`).## Warning: ggrepel: 108 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
# Plot relationship between EV1 and read depth
tab_annot %>%
ggplot(aes(-EV1_filter, wg_norm_depth, color=ecotype, shape=het_status)) +
geom_hline(data = sim_average, aes(yintercept = wg_norm_depth), linetype = "dashed", color = "grey", linewidth=0.25) +
geom_point(alpha=0.5, size=3) +
scale_color_manual(values=c("#d73027","#0072b2")) +
geom_label_repel(aes(label = acronym),
size = 3,
box.padding = 0.5,
point.padding = 0.5,
max.overlaps = 20,
nudge_x = 0.001,
segment.color = 'grey50') +
theme_cowplot(20) +
panel_border(color="black", size=0.75) +
xlab(paste("-",PC1_lab_filter, sep="")) +
ylab("MYH3C3 normalized depth")## Warning: Removed 29 rows containing missing values or values outside the scale range
## (`geom_point()`).## Warning: Removed 29 rows containing missing values or values outside the scale range
## (`geom_label_repel()`).## Warning: ggrepel: 96 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
# Plot relationship between EV1 and heterozygosity
tab_annot %>%
ggplot(aes(-EV1_filter, prop_het, color=ecotype, shape=het_status)) +
geom_point(alpha=0.5, size=3) +
scale_color_manual(values=c("#d73027","#0072b2")) +
geom_label_repel(aes(label = acronym),
size = 3,
box.padding = 0.5,
point.padding = 0.5,
max.overlaps = 20,
nudge_x = 0.001,
segment.color = 'grey50') +
theme_cowplot(20) +
panel_border(color="black", size=0.75) +
xlab(paste("-",PC1_lab_filter, sep="")) +
ylab("Proportion heterozygous sites")## Warning: Removed 29 rows containing missing values or values outside the scale range
## (`geom_point()`).## Warning: Removed 29 rows containing missing values or values outside the scale range
## (`geom_label_repel()`).## Warning: ggrepel: 119 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
# Plot relationship between EV1 and F (inbreeding coefficient)
tab_annot %>%
ggplot(aes(-EV1_filter, F, color=ecotype, shape=het_status)) +
geom_point(alpha=0.5, size=3) +
scale_color_manual(values=c("#d73027","#0072b2")) +
geom_label_repel(aes(label = acronym),
size = 3,
box.padding = 0.5,
point.padding = 0.5,
max.overlaps = 20,
nudge_x = 0.001,
segment.color = 'grey50') +
theme_cowplot(20) +
panel_border(color="black", size=0.75) +
xlab(paste("-",PC1_lab_filter, sep="")) +
ylab("F (Inbreeding)")## Warning: Removed 29 rows containing missing values or values outside the scale range
## (`geom_point()`).## Warning: Removed 29 rows containing missing values or values outside the scale range
## (`geom_label_repel()`).## Warning: ggrepel: 119 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
# Get list of candidate California homoyzgotes
tab_annot %>%
filter(EV2_filter < -0.025, het_status == "likely-hom")## sample.id EV1 EV2 EV3 EV4
## 1 BIGR_1_32_2007#03 0.0100751593 0.11578563 0.01831565 -0.10427624
## 2 BIGR_52_54_2008#02 0.0091435651 0.14479520 0.02631595 0.24085546
## 3 BIGR|1_32|2007#01 0.0188560556 0.16358342 0.02788093 -0.07274894
## 4 BIGR|3_63|2007#14 0.0162503009 0.16443493 0.03441498 0.01335552
## 5 BIGR|52_54|2007#04 0.0199480738 0.20172835 0.03309421 0.37149940
## 6 BIGR|52_54|2007#05 0.0168073085 0.19977535 0.03250645 0.34986706
## 7 BIGR|52_54|2007#12 0.0204752622 0.18804374 0.03377389 0.18704503
## 8 BIGR|52_54|2007#17 0.0193218937 0.20385198 0.03233429 0.37091308
## 9 CERC|X|X#04 0.0150473718 0.18145013 0.02665453 -0.16195633
## 10 ERBC|X|X#8770 0.0218436125 0.19946133 0.02331171 -0.20130306
## 11 FRIC_X_2003#C10 0.0210042351 0.18692614 0.03330537 -0.23635681
## 12 FRIL|X|X#05 0.0220998474 0.19757231 0.03149441 -0.24620270
## 13 GARC|X|X#711 0.0171456832 0.19325512 0.02681144 -0.18328100
## 14 LSOL|X|2012#04 0.0179523696 0.18814068 0.02989656 -0.09516079
## 15 OLNY_X_2007#03 0.0061191064 0.08980188 0.02003394 -0.10176181
## 16 PINC|X|X#03 0.0165511404 0.18198584 0.02801136 -0.17855641
## 17 SACK|X|2010#0898 0.0173504106 0.17199851 0.02840399 0.22559109
## 18 SALS|X|X#01 0.0181552697 0.18102229 0.02828708 -0.06651498
## 19 SAPC|X|X#01 0.0196026242 0.18944466 0.03052582 -0.19929522
## 20 SCRM|X|2010#873 0.0161646657 0.17495012 0.03989625 0.07423526
## 21 SCRS|X|2009#8253 0.0176966730 0.19152732 0.04437520 0.10215907
## 22 SJCR|X|2009#8212 0.0181230015 0.16922501 0.03711147 0.04985315
## 23 SLMW|X|2001#0918 0.0142021341 0.17006157 0.03816982 0.07694062
## 24 SSMC|X|2010#01 0.0171410153 0.17834774 0.03023856 -0.16256980
## 25 SUNC|X|X#04 0.0174810880 0.18174345 0.02659797 -0.19299190
## 26 WMSO_X_2002#bigf -0.0007224793 0.07434659 0.02166504 0.04705335
## samp desc region chr startpos endpos
## 1 BIGR_1_32_2007_03 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 2 BIGR_52_54_2008_02 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 3 BIGR_1_32_2007_01 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 4 BIGR_3_63_2007_14 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 5 BIGR_52_54_2007_04 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 6 BIGR_52_54_2007_05 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 7 BIGR_52_54_2007_12 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 8 BIGR_52_54_2007_17 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 9 CERC_X_X_04 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 10 ERBC_X_X_8770 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 11 FRIC_X_2003_C10 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 12 FRIL_X_X_05 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 13 GARC_X_X_711 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 14 LSOL_X_2012_04 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 15 OLNY_X_2007_03 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 16 PINC_X_X_03 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 17 SACK_X_2010_0898 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 18 SALS_X_X_01 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 19 SAPC_X_X_01 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 20 SCRM_X_2010_873 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 21 SCRS_X_2009_8253 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 22 SJCR_X_2009_8212 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 23 SLMW_X_2001_0918 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 24 SSMC_X_2010_01 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 25 SUNC_X_X_04 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 26 WMSO_X_2002_bigf MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## numreads covbases coverage meandepth meanbaseq meanmapq acronym type
## 1 553 7912 73.7647 3.64833 21.6 46.7 BIGR real
## 2 971 8886 82.8454 6.50951 21.4 45.9 BIGR real
## 3 1032 8865 82.6496 7.12400 27.0 49.7 BIGR real
## 4 1634 9201 85.7822 11.25380 26.9 50.7 BIGR real
## 5 1057 8997 83.8803 7.21108 26.1 49.2 BIGR real
## 6 1222 9065 84.5143 8.39120 26.2 50.3 BIGR real
## 7 1445 8993 83.8430 9.99291 26.9 49.1 BIGR real
## 8 1019 8866 82.6590 6.96970 26.7 50.8 BIGR real
## 9 1301 9011 84.0108 8.97017 26.2 49.7 CERC real
## 10 1293 8508 79.3213 8.87749 26.2 50.8 ERBC real
## 11 1950 9238 86.1272 13.45810 21.9 49.1 FRIC real
## 12 1259 8728 81.3724 8.61001 24.9 51.1 FRIL real
## 13 1318 8707 81.1766 9.04326 26.1 49.9 GARC real
## 14 1107 9052 84.3931 7.56610 24.9 49.8 LSOL real
## 15 462 7475 69.6905 3.05146 20.4 47.1 OLNY real
## 16 1122 8821 82.2394 7.73690 26.3 50.8 PINC real
## 17 1452 8871 82.7056 9.95338 26.1 50.5 SACK real
## 18 1765 8993 83.8430 12.14660 25.8 50.9 SALS real
## 19 1087 8903 83.0039 7.46439 26.1 50.3 SAPC real
## 20 1269 8830 82.3233 8.72142 26.4 50.5 SCRM real
## 21 1392 8753 81.6054 9.64059 26.6 50.7 SCRS real
## 22 1177 9260 86.3323 8.14768 26.6 49.0 SJCR real
## 23 1252 8415 78.4542 8.58848 23.9 52.1 SLMW real
## 24 1467 9016 84.0574 9.99030 25.4 50.2 SSMC real
## 25 1206 9086 84.7101 8.25853 26.0 50.8 SUNC real
## 26 441 7512 70.0354 2.93735 22.2 45.0 WMSO real
## weighted_mean_depth pop coord_approx
## 1 7.478171 Big River, CA <NA>
## 2 7.524425 Big River, CA <NA>
## 3 5.188078 Big River, CA <NA>
## 4 5.252176 Big River, CA <NA>
## 5 5.034470 Big River, CA <NA>
## 6 5.888779 Big River, CA <NA>
## 7 5.732498 Big River, CA <NA>
## 8 4.859141 Big River, CA <NA>
## 9 5.619699 Cerrito Creek, Northern California <NA>
## 10 6.101296 El Rosario Boca, Mexico Y
## 11 9.623590 Friant River <NA>
## 12 5.989229 Friant River <NA>
## 13 5.887613 Garrity Creek, Northern California <NA>
## 14 6.703666 Lake Solano, CA <NA>
## 15 8.340684 Olney Creek, California <NA>
## 16 5.038420 Pinole Creek, Northern California <NA>
## 17 5.840406 San Antonio Creek, VAFB, CA <NA>
## 18 6.643173 Salinas River, CA <NA>
## 19 4.959425 San Pablo Creek, Northern California <NA>
## 20 6.017638 Santa Clara River mouth, LA, CA <NA>
## 21 6.563724 Santa Clara River, LA, CA Y
## 22 5.821765 San Jacinto River, San Bernadino, CA Y
## 23 5.752081 Sugarloaf Meadow, San Bernadino, CA Y
## 24 6.874873 San Simeon Creek, CA Y
## 25 5.200986 Suisun Creek, Northern California Y
## 26 7.117236 Williamsoni, CA; no plates <NA>
## GPS_north GPS_east mar_fresh notes water_type
## 1 39.289 -123.747 M <NA> Marine
## 2 39.317 -123.686 F <NA> River
## 3 39.289 -123.747 M <NA> Marine
## 4 39.289 -123.747 M <NA> Marine
## 5 39.317 -123.686 F <NA> River
## 6 39.317 -123.686 F <NA> River
## 7 39.317 -123.686 F <NA> River
## 8 39.317 -123.686 F <NA> River
## 9 37.901 -122.281 F <NA> Freshwater
## 10 30.036 -115.774 F <NA> <NA>
## 11 36.980 -119.731 F Completely plated River
## 12 36.980 -119.731 F Low plated River
## 13 38.001 -122.322 F <NA> Creek
## 14 38.495 -122.033 F <NA> Lake
## 15 40.528 -122.384 F <NA> River
## 16 37.963 -122.202 F <NA> Creek
## 17 34.783 -120.536 F <NA> River
## 18 36.647 -121.702 F <NA> River
## 19 37.966 -122.320 F <NA> Creek
## 20 34.236 -119.257 M <NA> Marine
## 21 34.436 -118.612 F <NA> Fresh
## 22 33.765 -117.208 F <NA> River
## 23 34.179 -116.830 F <NA> Fresh
## 24 35.608 -121.091 F <NA> River
## 25 38.225 -122.107 F <NA> Creek
## 26 34.435 -118.198 F <NA> Fresh
## PNW_independent_MvsF_c150 NorthEurope_independent_MvsF_c151
## 1 NA NA
## 2 NA NA
## 3 NA NA
## 4 NA NA
## 5 NA NA
## 6 NA NA
## 7 NA NA
## 8 NA NA
## 9 NA NA
## 10 NA NA
## 11 NA NA
## 12 NA NA
## 13 NA NA
## 14 NA NA
## 15 NA NA
## 16 NA NA
## 17 NA NA
## 18 NA NA
## 19 NA NA
## 20 NA NA
## 21 NA NA
## 22 NA NA
## 23 NA NA
## 24 NA NA
## 25 NA NA
## 26 NA NA
## CaliforniaFreshwater_vs_AllPacificMarine_c153 c154_globalsuperglacial_FvsM
## 1 1 NA
## 2 0 NA
## 3 NA NA
## 4 NA NA
## 5 NA NA
## 6 NA NA
## 7 NA NA
## 8 NA NA
## 9 0 NA
## 10 0 NA
## 11 NA NA
## 12 0 NA
## 13 0 NA
## 14 0 NA
## 15 0 NA
## 16 0 NA
## 17 0 NA
## 18 0 NA
## 19 0 NA
## 20 1 NA
## 21 0 NA
## 22 0 NA
## 23 0 NA
## 24 0 0
## 25 0 NA
## 26 0 NA
## c155_global_FvsM used_joint_genotyping used _river_comparisons
## 1 1 Yes Yes
## 2 0 Yes Yes
## 3 NA Yes Yes
## 4 NA Yes Yes
## 5 NA Yes Yes
## 6 NA Yes Yes
## 7 NA Yes Yes
## 8 NA Yes Yes
## 9 0 Yes <NA>
## 10 0 Yes <NA>
## 11 NA Yes <NA>
## 12 0 Yes <NA>
## 13 0 Yes <NA>
## 14 0 Yes <NA>
## 15 0 Yes <NA>
## 16 0 Yes <NA>
## 17 0 Yes <NA>
## 18 0 Yes <NA>
## 19 0 Yes <NA>
## 20 1 Yes <NA>
## 21 0 Yes <NA>
## 22 0 Yes <NA>
## 23 0 Yes <NA>
## 24 0 Yes <NA>
## 25 0 Yes <NA>
## 26 0 Yes <NA>
## used_pilot_analysis wg_norm_depth ecotype sex samp_length O(HOM)
## 1 <NA> 0.4878640 Marine female 17 2830
## 2 <NA> 0.8651173 Freshwater female 18 2984
## 3 <NA> 1.3731481 Marine female 17 3307
## 4 <NA> 2.1426930 Marine female 17 3382
## 5 <NA> 1.4323414 Freshwater female 18 3438
## 6 <NA> 1.4249474 Freshwater female 18 3466
## 7 <NA> 1.7432035 Freshwater female 18 3300
## 8 <NA> 1.4343480 Freshwater female 18 3414
## 9 <NA> 1.5962013 Freshwater female 11 3377
## 10 <NA> 1.4550171 Freshwater female 13 3548
## 11 <NA> 1.3984490 Freshwater female 15 3477
## 12 <NA> 1.4375823 Freshwater female 11 3537
## 13 <NA> 1.5359807 Freshwater female 12 3439
## 14 <NA> 1.1286510 Freshwater female 14 3336
## 15 <NA> 0.3658525 Freshwater female 14 2349
## 16 <NA> 1.5355805 Freshwater female 11 3410
## 17 <NA> 1.7042274 Freshwater female 16 3465
## 18 <NA> 1.8284334 Freshwater female 11 3364
## 19 <NA> 1.5050920 Freshwater female 11 3463
## 20 <NA> 1.4493094 Marine female 15 3360
## 21 <NA> 1.4687684 Freshwater female 16 3519
## 22 <NA> 1.3995207 Freshwater female 16 3377
## 23 <NA> 1.4931082 Freshwater female 16 3399
## 24 <NA> 1.4531613 Freshwater female 14 3335
## 25 <NA> 1.5878779 Freshwater female 11 3390
## 26 <NA> 0.4127094 Freshwater female 16 2122
## E(HOM) N_SITES F prop_hom prop_het het_status EV1_filter
## 1 2393.5 2911 0.84347 0.9721745 0.027825490 likely-hom 0.009546813
## 2 2493.0 3040 0.89762 0.9815789 0.018421053 likely-hom 0.008558535
## 3 2870.1 3509 0.68381 0.9424337 0.057566258 likely-hom 0.018374936
## 4 2882.7 3518 0.78594 0.9613417 0.038658329 likely-hom 0.015607905
## 5 2857.3 3497 0.90777 0.9831284 0.016871604 likely-hom 0.019485042
## 6 2919.5 3575 0.83372 0.9695105 0.030489510 likely-hom 0.016246984
## 7 2906.7 3561 0.60110 0.9267060 0.073294019 likely-hom 0.019994415
## 8 2832.0 3466 0.91798 0.9849971 0.015002885 likely-hom 0.018840145
## 9 2953.4 3609 0.64613 0.9357163 0.064283735 likely-hom 0.014492910
## 10 2954.2 3611 0.90408 0.9825533 0.017446691 likely-hom 0.021448016
## 11 2892.1 3537 0.90696 0.9830365 0.016963528 likely-hom 0.020476656
## 12 2950.9 3608 0.89194 0.9803215 0.019678492 likely-hom 0.021586141
## 13 2921.1 3570 0.79812 0.9633053 0.036694678 likely-hom 0.016493543
## 14 2957.8 3620 0.57111 0.9215470 0.078453039 likely-hom 0.017424375
## 15 1969.8 2372 0.94282 0.9903035 0.009696459 likely-hom 0.005606697
## 16 2877.2 3519 0.83017 0.9690253 0.030974709 likely-hom 0.015893340
## 17 2896.1 3541 0.88215 0.9785371 0.021462864 likely-hom 0.016888114
## 18 2992.8 3656 0.55968 0.9201313 0.079868709 likely-hom 0.017667124
## 19 2879.6 3519 0.91242 0.9840864 0.015913612 likely-hom 0.019073509
## 20 2905.6 3556 0.69863 0.9448819 0.055118110 likely-hom 0.015519429
## 21 2928.5 3589 0.89401 0.9804960 0.019504040 likely-hom 0.017058849
## 22 2916.7 3570 0.70457 0.9459384 0.054061625 likely-hom 0.017472125
## 23 2849.0 3479 0.87301 0.9770049 0.022995114 likely-hom 0.013551706
## 24 2885.8 3531 0.69622 0.9444916 0.055508355 likely-hom 0.016522975
## 25 2885.6 3524 0.79010 0.9619750 0.038024972 likely-hom 0.016938094
## 26 1764.2 2131 0.97547 0.9957766 0.004223369 likely-hom -0.001315031
## EV2_filter EV3_filter EV4_filter
## 1 -0.11843145 0.10637948 0.06464475
## 2 -0.14876096 -0.24370123 0.17333823
## 3 -0.16703559 0.07210053 -0.07728973
## 4 -0.16835067 -0.01185221 -0.12689155
## 5 -0.20708829 -0.37565240 0.23755130
## 6 -0.20511225 -0.35305833 0.24246976
## 7 -0.19262885 -0.18516098 0.11130308
## 8 -0.20925397 -0.37454415 0.24857129
## 9 -0.18615652 0.16605975 0.04287893
## 10 -0.20445617 0.20657670 0.05838613
## 11 -0.19084870 0.23927903 0.13274268
## 12 -0.20173500 0.24919669 0.12662749
## 13 -0.19748182 0.18670337 0.09328415
## 14 -0.19260255 0.09769245 -0.01570642
## 15 -0.09186589 0.10387434 0.07147890
## 16 -0.18602453 0.18282818 0.09708258
## 17 -0.17701377 -0.23516977 -0.09265259
## 18 -0.18517259 0.06488567 -0.07658309
## 19 -0.19359763 0.20325700 0.12469692
## 20 -0.17863637 -0.07839920 -0.38149787
## 21 -0.19580976 -0.10793180 -0.48334857
## 22 -0.17284261 -0.05296481 -0.24755345
## 23 -0.17433837 -0.08285338 -0.38907754
## 24 -0.18226047 0.16421650 0.03677027
## 25 -0.18593108 0.19739168 0.10676946
## 26 -0.07622227 -0.04882559 -0.21633645# Get list of candidate California homoyzgotes that are the closest to the bay area
tab_annot %>%
filter(EV2_filter < -0.025, het_status == "likely-hom", GPS_north < 38.5, GPS_north>37)## sample.id EV1 EV2 EV3 EV4 samp
## 1 CERC|X|X#04 0.01504737 0.1814501 0.02665453 -0.16195633 CERC_X_X_04
## 2 GARC|X|X#711 0.01714568 0.1932551 0.02681144 -0.18328100 GARC_X_X_711
## 3 LSOL|X|2012#04 0.01795237 0.1881407 0.02989656 -0.09516079 LSOL_X_2012_04
## 4 PINC|X|X#03 0.01655114 0.1819858 0.02801136 -0.17855641 PINC_X_X_03
## 5 SAPC|X|X#01 0.01960262 0.1894447 0.03052582 -0.19929522 SAPC_X_X_01
## 6 SUNC|X|X#04 0.01748109 0.1817435 0.02659797 -0.19299190 SUNC_X_X_04
## desc region chr startpos endpos numreads covbases
## 1 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020 1301 9011
## 2 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020 1318 8707
## 3 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020 1107 9052
## 4 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020 1122 8821
## 5 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020 1087 8903
## 6 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020 1206 9086
## coverage meandepth meanbaseq meanmapq acronym type weighted_mean_depth
## 1 84.0108 8.97017 26.2 49.7 CERC real 5.619699
## 2 81.1766 9.04326 26.1 49.9 GARC real 5.887613
## 3 84.3931 7.56610 24.9 49.8 LSOL real 6.703666
## 4 82.2394 7.73690 26.3 50.8 PINC real 5.038420
## 5 83.0039 7.46439 26.1 50.3 SAPC real 4.959425
## 6 84.7101 8.25853 26.0 50.8 SUNC real 5.200986
## pop coord_approx GPS_north GPS_east
## 1 Cerrito Creek, Northern California <NA> 37.901 -122.281
## 2 Garrity Creek, Northern California <NA> 38.001 -122.322
## 3 Lake Solano, CA <NA> 38.495 -122.033
## 4 Pinole Creek, Northern California <NA> 37.963 -122.202
## 5 San Pablo Creek, Northern California <NA> 37.966 -122.320
## 6 Suisun Creek, Northern California Y 38.225 -122.107
## mar_fresh notes water_type PNW_independent_MvsF_c150
## 1 F <NA> Freshwater NA
## 2 F <NA> Creek NA
## 3 F <NA> Lake NA
## 4 F <NA> Creek NA
## 5 F <NA> Creek NA
## 6 F <NA> Creek NA
## NorthEurope_independent_MvsF_c151
## 1 NA
## 2 NA
## 3 NA
## 4 NA
## 5 NA
## 6 NA
## CaliforniaFreshwater_vs_AllPacificMarine_c153 c154_globalsuperglacial_FvsM
## 1 0 NA
## 2 0 NA
## 3 0 NA
## 4 0 NA
## 5 0 NA
## 6 0 NA
## c155_global_FvsM used_joint_genotyping used _river_comparisons
## 1 0 Yes <NA>
## 2 0 Yes <NA>
## 3 0 Yes <NA>
## 4 0 Yes <NA>
## 5 0 Yes <NA>
## 6 0 Yes <NA>
## used_pilot_analysis wg_norm_depth ecotype sex samp_length O(HOM) E(HOM)
## 1 <NA> 1.596201 Freshwater female 11 3377 2953.4
## 2 <NA> 1.535981 Freshwater female 12 3439 2921.1
## 3 <NA> 1.128651 Freshwater female 14 3336 2957.8
## 4 <NA> 1.535581 Freshwater female 11 3410 2877.2
## 5 <NA> 1.505092 Freshwater female 11 3463 2879.6
## 6 <NA> 1.587878 Freshwater female 11 3390 2885.6
## N_SITES F prop_hom prop_het het_status EV1_filter EV2_filter
## 1 3609 0.64613 0.9357163 0.06428374 likely-hom 0.01449291 -0.1861565
## 2 3570 0.79812 0.9633053 0.03669468 likely-hom 0.01649354 -0.1974818
## 3 3620 0.57111 0.9215470 0.07845304 likely-hom 0.01742438 -0.1926026
## 4 3519 0.83017 0.9690253 0.03097471 likely-hom 0.01589334 -0.1860245
## 5 3519 0.91242 0.9840864 0.01591361 likely-hom 0.01907351 -0.1935976
## 6 3524 0.79010 0.9619750 0.03802497 likely-hom 0.01693809 -0.1859311
## EV3_filter EV4_filter
## 1 0.16605975 0.04287893
## 2 0.18670337 0.09328415
## 3 0.09769245 -0.01570642
## 4 0.18282818 0.09708258
## 5 0.20325700 0.12469692
## 6 0.19739168 0.10676946# Get list of candidate reciprocal deletion alleles (freshwater haplotype but 3 copy locus)
tab_annot %>%
filter(EV1_filter < -0.025, het_status == "likely-hom", wg_norm_depth < 1)## sample.id EV1 EV2 EV3 EV4
## 1 COAT|X|2009#90234 -0.05449798 -0.02410928 0.01349470 0.0002058328
## 2 EDEN_X_2010#01 -0.05459037 -0.02388626 0.01330334 0.0009069261
## 3 LITC_23_32_2008#324 -0.05142116 -0.02235130 0.01390980 0.0010580689
## 4 QUIN_X_2003#02 -0.03965557 -0.01108763 -0.01249209 -0.0034433250
## 5 SDPY_X_2006#24 -0.05448931 -0.02413039 0.01289862 0.0012177776
## samp desc region chr startpos endpos
## 1 COAT_X_2009_90234 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 2 EDEN_X_2010_01 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 3 LITC_23_32_2008_324 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 4 QUIN_X_2003_02 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## 5 SDPY_X_2006_24 MYH3C3 chrXIX:2667295-2678020 chrXIX 2667295 2678020
## numreads covbases coverage meandepth meanbaseq meanmapq acronym type
## 1 596 9189 85.6703 4.15234 27.6 51.7 COAT real
## 2 969 9320 86.8917 6.72665 24.0 50.7 EDEN real
## 3 1250 9496 88.5325 8.62987 22.0 50.3 LITC real
## 4 835 8873 82.7242 5.62698 20.2 51.6 QUIN real
## 5 1177 9471 88.2995 8.16530 22.5 52.0 SDPY real
## weighted_mean_depth pop coord_approx GPS_north
## 1 5.146996 Coates Lake, Haida Gwaii <NA> 53.669
## 2 11.307505 Eden Lake, Haida Gwaii <NA> 53.845
## 3 8.777877 Little Campbell upstream <NA> 49.012
## 4 6.702309 Quinalt, Washington <NA> 47.471
## 5 11.755091 Serendipity Pond, Haida Gwaii <NA> 54.028
## GPS_east mar_fresh notes water_type PNW_independent_MvsF_c150
## 1 -132.880 F <NA> Fresh 0
## 2 -132.746 F <NA> Lake 0
## 3 -122.625 F <NA> River NA
## 4 -123.871 F <NA> Fresh 0
## 5 -131.761 F <NA> Fresh 0
## NorthEurope_independent_MvsF_c151
## 1 NA
## 2 NA
## 3 NA
## 4 NA
## 5 NA
## CaliforniaFreshwater_vs_AllPacificMarine_c153 c154_globalsuperglacial_FvsM
## 1 NA NA
## 2 NA NA
## 3 NA NA
## 4 NA 0
## 5 NA NA
## c155_global_FvsM used_joint_genotyping used _river_comparisons
## 1 NA Yes <NA>
## 2 NA Yes <NA>
## 3 NA Yes Yes
## 4 0 Yes <NA>
## 5 NA Yes <NA>
## used_pilot_analysis wg_norm_depth ecotype sex samp_length O(HOM) E(HOM)
## 1 <NA> 0.8067502 Freshwater female 17 3820 3139.3
## 2 <NA> 0.5948837 Freshwater female 14 3836 3155.0
## 3 <NA> 0.9831386 Freshwater female 19 3670 3031.0
## 4 <NA> 0.8395585 Freshwater female 14 3242 2683.3
## 5 <NA> 0.6946182 Freshwater female 14 3836 3160.1
## N_SITES F prop_hom prop_het het_status EV1_filter EV2_filter
## 1 3842 0.96869 0.9942738 0.005726184 likely-hom -0.05705539 0.02385962
## 2 3862 0.96322 0.9932677 0.006732263 likely-hom -0.05715463 0.02365214
## 3 3709 0.94248 0.9894850 0.010514964 likely-hom -0.05389208 0.02214684
## 4 3272 0.94904 0.9908313 0.009168704 likely-hom -0.04142741 0.01078058
## 5 3867 0.95615 0.9919834 0.008016550 likely-hom -0.05705723 0.02392578
## EV3_filter EV4_filter
## 1 -0.0006896213 0.0010690668
## 2 -0.0013086025 0.0012333431
## 3 -0.0013500294 0.0001571319
## 4 0.0050243160 -0.0130214279
## 5 -0.0015563032 0.0017423955